program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}})]
{
    func main<ios16>(tensor<fp32, [1, 77]> input_ids) {
            tensor<int32, []> var_5 = const()[name = tensor<string, []>("op_5"), val = tensor<int32, []>(-1)];
            tensor<bool, []> var_6 = const()[name = tensor<string, []>("op_6"), val = tensor<bool, []>(false)];
            tensor<string, []> cast_1_dtype_0 = const()[name = tensor<string, []>("cast_1_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, []> inputs_embeds_axis_0 = const()[name = tensor<string, []>("inputs_embeds_axis_0"), val = tensor<int32, []>(0)];
            tensor<int32, []> inputs_embeds_batch_dims_0 = const()[name = tensor<string, []>("inputs_embeds_batch_dims_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [49408, 1024]> text_encoder_text_model_embeddings_token_embedding_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_embeddings_token_embedding_weight_to_fp16"), val = tensor<fp16, [49408, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<int32, [1, 77]> cast_2 = cast(dtype = cast_1_dtype_0, x = input_ids)[name = tensor<string, []>("cast_2")];
            tensor<fp16, [1, 77, 1024]> inputs_embeds_cast = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = cast_2, x = text_encoder_text_model_embeddings_token_embedding_weight_to_fp16)[name = tensor<string, []>("inputs_embeds_cast")];
            tensor<fp16, [1, 77, 1024]> position_embeddings_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [19712]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101187712))), lut = tensor<fp16, [4]>([-0x1.16cp-7, -0x1.0f8p-11, 0x1.b8p-8, 0x1.5acp-5]), name = tensor<string, []>("position_embeddings_to_fp16_palettized"), shape = tensor<uint32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 1024]> input_3_cast = add(x = inputs_embeds_cast, y = position_embeddings_to_fp16_palettized)[name = tensor<string, []>("input_3_cast")];
            tensor<int32, [1]> hidden_states_1_axes_0 = const()[name = tensor<string, []>("hidden_states_1_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101207488)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101209600)))];
            tensor<fp16, []> var_15_to_fp16 = const()[name = tensor<string, []>("op_15_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 77, 1024]> hidden_states_1_cast = layer_norm(axes = hidden_states_1_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16, x = input_3_cast)[name = tensor<string, []>("hidden_states_1_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101211712))), lut = tensor<fp16, [4]>([-0x1.a2p-6, -0x1.ebp-8, 0x1.eb4p-8, 0x1.a24p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101473920)))];
            tensor<fp16, [1, 77, 1024]> linear_0_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_1_cast)[name = tensor<string, []>("linear_0_cast")];
            tensor<fp16, []> var_130_to_fp16 = const()[name = tensor<string, []>("op_130_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_5_cast = mul(x = linear_0_cast, y = var_130_to_fp16)[name = tensor<string, []>("tensor_5_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101476032))), lut = tensor<fp16, [4]>([-0x1.97cp-6, -0x1.df8p-8, 0x1.db4p-8, 0x1.97p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101738240)))];
            tensor<fp16, [1, 77, 1024]> linear_1_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_1_cast)[name = tensor<string, []>("linear_1_cast")];
            tensor<int32, [4]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_136_cast = reshape(shape = var_135, x = linear_1_cast)[name = tensor<string, []>("op_136_cast")];
            tensor<int32, [4]> var_137_perm_0 = const()[name = tensor<string, []>("op_137_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101740352))), lut = tensor<fp16, [4]>([-0x1.388p-6, -0x1.718p-8, 0x1.714p-8, 0x1.38p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102002560)))];
            tensor<fp16, [1, 77, 1024]> linear_2_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_1_cast)[name = tensor<string, []>("linear_2_cast")];
            tensor<int32, [4]> var_142 = const()[name = tensor<string, []>("op_142"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_143_cast = reshape(shape = var_142, x = linear_2_cast)[name = tensor<string, []>("op_143_cast")];
            tensor<int32, [4]> var_144_perm_0 = const()[name = tensor<string, []>("op_144_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_151 = const()[name = tensor<string, []>("op_151"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_152_cast = reshape(shape = var_151, x = tensor_5_cast)[name = tensor<string, []>("op_152_cast")];
            tensor<int32, [4]> var_153_perm_0 = const()[name = tensor<string, []>("op_153_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_155 = const()[name = tensor<string, []>("op_155"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_114 = transpose(perm = var_153_perm_0, x = var_152_cast)[name = tensor<string, []>("transpose_114")];
            tensor<fp16, [16, 77, 64]> query_states_1_cast = reshape(shape = var_155, x = transpose_114)[name = tensor<string, []>("query_states_1_cast")];
            tensor<int32, [3]> var_157 = const()[name = tensor<string, []>("op_157"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_113 = transpose(perm = var_137_perm_0, x = var_136_cast)[name = tensor<string, []>("transpose_113")];
            tensor<fp16, [16, 77, 64]> key_states_3_cast = reshape(shape = var_157, x = transpose_113)[name = tensor<string, []>("key_states_3_cast")];
            tensor<int32, [3]> var_159 = const()[name = tensor<string, []>("op_159"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_112 = transpose(perm = var_144_perm_0, x = var_143_cast)[name = tensor<string, []>("transpose_112")];
            tensor<fp16, [16, 77, 64]> value_states_3_cast = reshape(shape = var_159, x = transpose_112)[name = tensor<string, []>("value_states_3_cast")];
            tensor<int32, [3]> var_162_perm_0 = const()[name = tensor<string, []>("op_162_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_1_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_1_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_111 = transpose(perm = var_162_perm_0, x = key_states_3_cast)[name = tensor<string, []>("transpose_111")];
            tensor<fp16, [16, 77, 77]> attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = query_states_1_cast, y = transpose_111)[name = tensor<string, []>("attn_weights_1_cast")];
            tensor<int32, [4]> var_164 = const()[name = tensor<string, []>("op_164"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_165_cast = reshape(shape = var_164, x = attn_weights_1_cast)[name = tensor<string, []>("op_165_cast")];
            tensor<fp16, [1, 1, 77, 77]> op_57_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1483]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102004672))), lut = tensor<fp16, [4]>([-0x0p+0, -0x1.388p+13, -0x1.388p+13, -0x1.388p+13]), name = tensor<string, []>("op_57_to_fp16_palettized"), shape = tensor<uint32, [4]>([1, 1, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_3_cast = add(x = var_165_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_3_cast")];
            tensor<int32, [3]> var_170 = const()[name = tensor<string, []>("op_170"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_5_cast = reshape(shape = var_170, x = attn_weights_3_cast)[name = tensor<string, []>("input_5_cast")];
            tensor<fp16, [16, 77, 77]> input_7_cast = softmax(axis = var_5, x = input_5_cast)[name = tensor<string, []>("input_7_cast")];
            tensor<bool, []> attn_output_1_transpose_x_0 = const()[name = tensor<string, []>("attn_output_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_1_transpose_y_0 = const()[name = tensor<string, []>("attn_output_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_1_cast = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = input_7_cast, y = value_states_3_cast)[name = tensor<string, []>("attn_output_1_cast")];
            tensor<int32, [4]> var_175 = const()[name = tensor<string, []>("op_175"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_3_cast = reshape(shape = var_175, x = attn_output_1_cast)[name = tensor<string, []>("attn_output_3_cast")];
            tensor<int32, [4]> attn_output_5_perm_0 = const()[name = tensor<string, []>("attn_output_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_178 = const()[name = tensor<string, []>("op_178"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_110 = transpose(perm = attn_output_5_perm_0, x = attn_output_3_cast)[name = tensor<string, []>("transpose_110")];
            tensor<fp16, [1, 77, 1024]> input_9_cast = reshape(shape = var_178, x = transpose_110)[name = tensor<string, []>("input_9_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102006272))), lut = tensor<fp16, [4]>([-0x1.348p-6, -0x1.6ccp-8, 0x1.74p-8, 0x1.368p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102268480)))];
            tensor<fp16, [1, 77, 1024]> linear_3_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_palettized, x = input_9_cast)[name = tensor<string, []>("linear_3_cast")];
            tensor<fp16, [1, 77, 1024]> input_11_cast = add(x = input_3_cast, y = linear_3_cast)[name = tensor<string, []>("input_11_cast")];
            tensor<int32, [1]> input_13_axes_0 = const()[name = tensor<string, []>("input_13_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102270592)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102272704)))];
            tensor<fp16, [1, 77, 1024]> input_13_cast = layer_norm(axes = input_13_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16, x = input_11_cast)[name = tensor<string, []>("input_13_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102274816))), lut = tensor<fp16, [4]>([-0x1.508p-6, -0x1.93cp-8, 0x1.8ap-8, 0x1.4d8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103323456))), lut = tensor<fp16, [4]>([-0x1.27p-2, -0x1.decp-2, -0x1.e8cp+0, -0x1.4fcp-1]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_4_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16_palettized, x = input_13_cast)[name = tensor<string, []>("linear_4_cast")];
            tensor<string, []> input_17_mode_0 = const()[name = tensor<string, []>("input_17_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_17_cast = gelu(mode = input_17_mode_0, x = linear_4_cast)[name = tensor<string, []>("input_17_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103324544))), lut = tensor<fp16, [4]>([-0x1.018p-6, -0x1.32p-8, 0x1.328p-8, 0x1.02p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104373184)))];
            tensor<fp16, [1, 77, 1024]> linear_5_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16_palettized, x = input_17_cast)[name = tensor<string, []>("linear_5_cast")];
            tensor<fp16, [1, 77, 1024]> input_19_cast = add(x = input_11_cast, y = linear_5_cast)[name = tensor<string, []>("input_19_cast")];
            tensor<int32, [1]> hidden_states_7_axes_0 = const()[name = tensor<string, []>("hidden_states_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104375296)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104377408)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_7_cast = layer_norm(axes = hidden_states_7_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16, x = input_19_cast)[name = tensor<string, []>("hidden_states_7_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104379520))), lut = tensor<fp16, [4]>([-0x1.59p-6, -0x1.94p-8, 0x1.988p-8, 0x1.59cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104641728)))];
            tensor<fp16, [1, 77, 1024]> linear_6_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_7_cast)[name = tensor<string, []>("linear_6_cast")];
            tensor<fp16, []> var_217_to_fp16 = const()[name = tensor<string, []>("op_217_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_11_cast = mul(x = linear_6_cast, y = var_217_to_fp16)[name = tensor<string, []>("tensor_11_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104643840))), lut = tensor<fp16, [4]>([-0x1.5f8p-6, -0x1.9b8p-8, 0x1.9bcp-8, 0x1.6p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104906048)))];
            tensor<fp16, [1, 77, 1024]> linear_7_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_7_cast)[name = tensor<string, []>("linear_7_cast")];
            tensor<int32, [4]> var_222 = const()[name = tensor<string, []>("op_222"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_223_cast = reshape(shape = var_222, x = linear_7_cast)[name = tensor<string, []>("op_223_cast")];
            tensor<int32, [4]> var_224_perm_0 = const()[name = tensor<string, []>("op_224_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104908160))), lut = tensor<fp16, [4]>([-0x1.04p-6, -0x1.33p-8, 0x1.3a4p-8, 0x1.054p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105170368)))];
            tensor<fp16, [1, 77, 1024]> linear_8_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_7_cast)[name = tensor<string, []>("linear_8_cast")];
            tensor<int32, [4]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_230_cast = reshape(shape = var_229, x = linear_8_cast)[name = tensor<string, []>("op_230_cast")];
            tensor<int32, [4]> var_231_perm_0 = const()[name = tensor<string, []>("op_231_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_238 = const()[name = tensor<string, []>("op_238"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_239_cast = reshape(shape = var_238, x = tensor_11_cast)[name = tensor<string, []>("op_239_cast")];
            tensor<int32, [4]> var_240_perm_0 = const()[name = tensor<string, []>("op_240_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_242 = const()[name = tensor<string, []>("op_242"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_109 = transpose(perm = var_240_perm_0, x = var_239_cast)[name = tensor<string, []>("transpose_109")];
            tensor<fp16, [16, 77, 64]> query_states_3_cast = reshape(shape = var_242, x = transpose_109)[name = tensor<string, []>("query_states_3_cast")];
            tensor<int32, [3]> var_244 = const()[name = tensor<string, []>("op_244"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_108 = transpose(perm = var_224_perm_0, x = var_223_cast)[name = tensor<string, []>("transpose_108")];
            tensor<fp16, [16, 77, 64]> key_states_7_cast = reshape(shape = var_244, x = transpose_108)[name = tensor<string, []>("key_states_7_cast")];
            tensor<int32, [3]> var_246 = const()[name = tensor<string, []>("op_246"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_107 = transpose(perm = var_231_perm_0, x = var_230_cast)[name = tensor<string, []>("transpose_107")];
            tensor<fp16, [16, 77, 64]> value_states_7_cast = reshape(shape = var_246, x = transpose_107)[name = tensor<string, []>("value_states_7_cast")];
            tensor<int32, [3]> var_249_perm_0 = const()[name = tensor<string, []>("op_249_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_7_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_7_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_106 = transpose(perm = var_249_perm_0, x = key_states_7_cast)[name = tensor<string, []>("transpose_106")];
            tensor<fp16, [16, 77, 77]> attn_weights_7_cast = matmul(transpose_x = attn_weights_7_transpose_x_0, transpose_y = attn_weights_7_transpose_y_0, x = query_states_3_cast, y = transpose_106)[name = tensor<string, []>("attn_weights_7_cast")];
            tensor<int32, [4]> var_251 = const()[name = tensor<string, []>("op_251"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_252_cast = reshape(shape = var_251, x = attn_weights_7_cast)[name = tensor<string, []>("op_252_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_9_cast = add(x = var_252_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_9_cast")];
            tensor<int32, [3]> var_257 = const()[name = tensor<string, []>("op_257"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_21_cast = reshape(shape = var_257, x = attn_weights_9_cast)[name = tensor<string, []>("input_21_cast")];
            tensor<fp16, [16, 77, 77]> input_23_cast = softmax(axis = var_5, x = input_21_cast)[name = tensor<string, []>("input_23_cast")];
            tensor<bool, []> attn_output_7_transpose_x_0 = const()[name = tensor<string, []>("attn_output_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_7_transpose_y_0 = const()[name = tensor<string, []>("attn_output_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_7_cast = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = input_23_cast, y = value_states_7_cast)[name = tensor<string, []>("attn_output_7_cast")];
            tensor<int32, [4]> var_262 = const()[name = tensor<string, []>("op_262"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_9_cast = reshape(shape = var_262, x = attn_output_7_cast)[name = tensor<string, []>("attn_output_9_cast")];
            tensor<int32, [4]> attn_output_11_perm_0 = const()[name = tensor<string, []>("attn_output_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_265 = const()[name = tensor<string, []>("op_265"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_105 = transpose(perm = attn_output_11_perm_0, x = attn_output_9_cast)[name = tensor<string, []>("transpose_105")];
            tensor<fp16, [1, 77, 1024]> input_25_cast = reshape(shape = var_265, x = transpose_105)[name = tensor<string, []>("input_25_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105172480))), lut = tensor<fp16, [4]>([-0x1.0c4p-6, -0x1.3f8p-8, 0x1.428p-8, 0x1.0d4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105434688)))];
            tensor<fp16, [1, 77, 1024]> linear_9_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_palettized, x = input_25_cast)[name = tensor<string, []>("linear_9_cast")];
            tensor<fp16, [1, 77, 1024]> input_27_cast = add(x = input_19_cast, y = linear_9_cast)[name = tensor<string, []>("input_27_cast")];
            tensor<int32, [1]> input_29_axes_0 = const()[name = tensor<string, []>("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105436800)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105438912)))];
            tensor<fp16, [1, 77, 1024]> input_29_cast = layer_norm(axes = input_29_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16, x = input_27_cast)[name = tensor<string, []>("input_29_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105441024))), lut = tensor<fp16, [4]>([-0x1.45p-6, -0x1.76p-8, 0x1.9ecp-8, 0x1.504p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(106489664))), lut = tensor<fp16, [4]>([-0x1.bep-2, -0x1.3a4p-5, -0x1.16cp-1, -0x1.534p-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_10_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16_palettized, x = input_29_cast)[name = tensor<string, []>("linear_10_cast")];
            tensor<string, []> input_33_mode_0 = const()[name = tensor<string, []>("input_33_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_33_cast = gelu(mode = input_33_mode_0, x = linear_10_cast)[name = tensor<string, []>("input_33_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(106490752))), lut = tensor<fp16, [4]>([-0x1.0f8p-6, -0x1.42cp-8, 0x1.44cp-8, 0x1.1p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107539392)))];
            tensor<fp16, [1, 77, 1024]> linear_11_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16_palettized, x = input_33_cast)[name = tensor<string, []>("linear_11_cast")];
            tensor<fp16, [1, 77, 1024]> input_35_cast = add(x = input_27_cast, y = linear_11_cast)[name = tensor<string, []>("input_35_cast")];
            tensor<int32, [1]> hidden_states_13_axes_0 = const()[name = tensor<string, []>("hidden_states_13_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107541504)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107543616)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_13_cast = layer_norm(axes = hidden_states_13_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16, x = input_35_cast)[name = tensor<string, []>("hidden_states_13_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107545728))), lut = tensor<fp16, [4]>([-0x1.588p-6, -0x1.92cp-8, 0x1.8fcp-8, 0x1.57cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107807936)))];
            tensor<fp16, [1, 77, 1024]> linear_12_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_13_cast)[name = tensor<string, []>("linear_12_cast")];
            tensor<fp16, []> var_304_to_fp16 = const()[name = tensor<string, []>("op_304_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_17_cast = mul(x = linear_12_cast, y = var_304_to_fp16)[name = tensor<string, []>("tensor_17_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(107810048))), lut = tensor<fp16, [4]>([-0x1.5e8p-6, -0x1.9a8p-8, 0x1.9p-8, 0x1.5b8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108072256)))];
            tensor<fp16, [1, 77, 1024]> linear_13_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_13_cast)[name = tensor<string, []>("linear_13_cast")];
            tensor<int32, [4]> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_310_cast = reshape(shape = var_309, x = linear_13_cast)[name = tensor<string, []>("op_310_cast")];
            tensor<int32, [4]> var_311_perm_0 = const()[name = tensor<string, []>("op_311_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108074368))), lut = tensor<fp16, [4]>([-0x1.0ep-6, -0x1.3fp-8, 0x1.42p-8, 0x1.0e8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108336576)))];
            tensor<fp16, [1, 77, 1024]> linear_14_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_13_cast)[name = tensor<string, []>("linear_14_cast")];
            tensor<int32, [4]> var_316 = const()[name = tensor<string, []>("op_316"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_317_cast = reshape(shape = var_316, x = linear_14_cast)[name = tensor<string, []>("op_317_cast")];
            tensor<int32, [4]> var_318_perm_0 = const()[name = tensor<string, []>("op_318_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_325 = const()[name = tensor<string, []>("op_325"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_326_cast = reshape(shape = var_325, x = tensor_17_cast)[name = tensor<string, []>("op_326_cast")];
            tensor<int32, [4]> var_327_perm_0 = const()[name = tensor<string, []>("op_327_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_329 = const()[name = tensor<string, []>("op_329"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_104 = transpose(perm = var_327_perm_0, x = var_326_cast)[name = tensor<string, []>("transpose_104")];
            tensor<fp16, [16, 77, 64]> query_states_5_cast = reshape(shape = var_329, x = transpose_104)[name = tensor<string, []>("query_states_5_cast")];
            tensor<int32, [3]> var_331 = const()[name = tensor<string, []>("op_331"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_103 = transpose(perm = var_311_perm_0, x = var_310_cast)[name = tensor<string, []>("transpose_103")];
            tensor<fp16, [16, 77, 64]> key_states_11_cast = reshape(shape = var_331, x = transpose_103)[name = tensor<string, []>("key_states_11_cast")];
            tensor<int32, [3]> var_333 = const()[name = tensor<string, []>("op_333"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_102 = transpose(perm = var_318_perm_0, x = var_317_cast)[name = tensor<string, []>("transpose_102")];
            tensor<fp16, [16, 77, 64]> value_states_11_cast = reshape(shape = var_333, x = transpose_102)[name = tensor<string, []>("value_states_11_cast")];
            tensor<int32, [3]> var_336_perm_0 = const()[name = tensor<string, []>("op_336_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_13_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_13_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_101 = transpose(perm = var_336_perm_0, x = key_states_11_cast)[name = tensor<string, []>("transpose_101")];
            tensor<fp16, [16, 77, 77]> attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = query_states_5_cast, y = transpose_101)[name = tensor<string, []>("attn_weights_13_cast")];
            tensor<int32, [4]> var_338 = const()[name = tensor<string, []>("op_338"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_339_cast = reshape(shape = var_338, x = attn_weights_13_cast)[name = tensor<string, []>("op_339_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_15_cast = add(x = var_339_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_15_cast")];
            tensor<int32, [3]> var_344 = const()[name = tensor<string, []>("op_344"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_37_cast = reshape(shape = var_344, x = attn_weights_15_cast)[name = tensor<string, []>("input_37_cast")];
            tensor<fp16, [16, 77, 77]> input_39_cast = softmax(axis = var_5, x = input_37_cast)[name = tensor<string, []>("input_39_cast")];
            tensor<bool, []> attn_output_13_transpose_x_0 = const()[name = tensor<string, []>("attn_output_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_13_transpose_y_0 = const()[name = tensor<string, []>("attn_output_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_13_cast = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = input_39_cast, y = value_states_11_cast)[name = tensor<string, []>("attn_output_13_cast")];
            tensor<int32, [4]> var_349 = const()[name = tensor<string, []>("op_349"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_15_cast = reshape(shape = var_349, x = attn_output_13_cast)[name = tensor<string, []>("attn_output_15_cast")];
            tensor<int32, [4]> attn_output_17_perm_0 = const()[name = tensor<string, []>("attn_output_17_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_352 = const()[name = tensor<string, []>("op_352"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_100 = transpose(perm = attn_output_17_perm_0, x = attn_output_15_cast)[name = tensor<string, []>("transpose_100")];
            tensor<fp16, [1, 77, 1024]> input_41_cast = reshape(shape = var_352, x = transpose_100)[name = tensor<string, []>("input_41_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108338688))), lut = tensor<fp16, [4]>([-0x1.114p-6, -0x1.46p-8, 0x1.42p-8, 0x1.0fcp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108600896)))];
            tensor<fp16, [1, 77, 1024]> linear_15_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_palettized, x = input_41_cast)[name = tensor<string, []>("linear_15_cast")];
            tensor<fp16, [1, 77, 1024]> input_43_cast = add(x = input_35_cast, y = linear_15_cast)[name = tensor<string, []>("input_43_cast")];
            tensor<int32, [1]> input_45_axes_0 = const()[name = tensor<string, []>("input_45_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108603008)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108605120)))];
            tensor<fp16, [1, 77, 1024]> input_45_cast = layer_norm(axes = input_45_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16, x = input_43_cast)[name = tensor<string, []>("input_45_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108607232))), lut = tensor<fp16, [4]>([-0x1.468p-6, -0x1.7fcp-8, 0x1.914p-8, 0x1.4bp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109655872))), lut = tensor<fp16, [4]>([-0x1.b64p-2, -0x1.5fp-2, -0x1.dacp-3, -0x1.10cp-1]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_16_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16_palettized, x = input_45_cast)[name = tensor<string, []>("linear_16_cast")];
            tensor<string, []> input_49_mode_0 = const()[name = tensor<string, []>("input_49_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_49_cast = gelu(mode = input_49_mode_0, x = linear_16_cast)[name = tensor<string, []>("input_49_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109656960))), lut = tensor<fp16, [4]>([-0x1.134p-6, -0x1.488p-8, 0x1.4a4p-8, 0x1.138p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110705600)))];
            tensor<fp16, [1, 77, 1024]> linear_17_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16_palettized, x = input_49_cast)[name = tensor<string, []>("linear_17_cast")];
            tensor<fp16, [1, 77, 1024]> input_51_cast = add(x = input_43_cast, y = linear_17_cast)[name = tensor<string, []>("input_51_cast")];
            tensor<int32, [1]> hidden_states_19_axes_0 = const()[name = tensor<string, []>("hidden_states_19_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110707712)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110709824)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_19_cast = layer_norm(axes = hidden_states_19_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16, x = input_51_cast)[name = tensor<string, []>("hidden_states_19_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110711936))), lut = tensor<fp16, [4]>([-0x1.634p-6, -0x1.a6p-8, 0x1.a1cp-8, 0x1.62cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110974144)))];
            tensor<fp16, [1, 77, 1024]> linear_18_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("linear_18_cast")];
            tensor<fp16, []> var_391_to_fp16 = const()[name = tensor<string, []>("op_391_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_23_cast = mul(x = linear_18_cast, y = var_391_to_fp16)[name = tensor<string, []>("tensor_23_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(110976256))), lut = tensor<fp16, [4]>([-0x1.64cp-6, -0x1.a4p-8, 0x1.a14p-8, 0x1.638p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111238464)))];
            tensor<fp16, [1, 77, 1024]> linear_19_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("linear_19_cast")];
            tensor<int32, [4]> var_396 = const()[name = tensor<string, []>("op_396"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_397_cast = reshape(shape = var_396, x = linear_19_cast)[name = tensor<string, []>("op_397_cast")];
            tensor<int32, [4]> var_398_perm_0 = const()[name = tensor<string, []>("op_398_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111240576))), lut = tensor<fp16, [4]>([-0x1.14p-6, -0x1.49cp-8, 0x1.464p-8, 0x1.12cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111502784)))];
            tensor<fp16, [1, 77, 1024]> linear_20_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_19_cast)[name = tensor<string, []>("linear_20_cast")];
            tensor<int32, [4]> var_403 = const()[name = tensor<string, []>("op_403"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_404_cast = reshape(shape = var_403, x = linear_20_cast)[name = tensor<string, []>("op_404_cast")];
            tensor<int32, [4]> var_405_perm_0 = const()[name = tensor<string, []>("op_405_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_412 = const()[name = tensor<string, []>("op_412"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_413_cast = reshape(shape = var_412, x = tensor_23_cast)[name = tensor<string, []>("op_413_cast")];
            tensor<int32, [4]> var_414_perm_0 = const()[name = tensor<string, []>("op_414_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_416 = const()[name = tensor<string, []>("op_416"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_99 = transpose(perm = var_414_perm_0, x = var_413_cast)[name = tensor<string, []>("transpose_99")];
            tensor<fp16, [16, 77, 64]> query_states_7_cast = reshape(shape = var_416, x = transpose_99)[name = tensor<string, []>("query_states_7_cast")];
            tensor<int32, [3]> var_418 = const()[name = tensor<string, []>("op_418"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_98 = transpose(perm = var_398_perm_0, x = var_397_cast)[name = tensor<string, []>("transpose_98")];
            tensor<fp16, [16, 77, 64]> key_states_15_cast = reshape(shape = var_418, x = transpose_98)[name = tensor<string, []>("key_states_15_cast")];
            tensor<int32, [3]> var_420 = const()[name = tensor<string, []>("op_420"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_97 = transpose(perm = var_405_perm_0, x = var_404_cast)[name = tensor<string, []>("transpose_97")];
            tensor<fp16, [16, 77, 64]> value_states_15_cast = reshape(shape = var_420, x = transpose_97)[name = tensor<string, []>("value_states_15_cast")];
            tensor<int32, [3]> var_423_perm_0 = const()[name = tensor<string, []>("op_423_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_19_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_19_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_19_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_19_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_96 = transpose(perm = var_423_perm_0, x = key_states_15_cast)[name = tensor<string, []>("transpose_96")];
            tensor<fp16, [16, 77, 77]> attn_weights_19_cast = matmul(transpose_x = attn_weights_19_transpose_x_0, transpose_y = attn_weights_19_transpose_y_0, x = query_states_7_cast, y = transpose_96)[name = tensor<string, []>("attn_weights_19_cast")];
            tensor<int32, [4]> var_425 = const()[name = tensor<string, []>("op_425"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_426_cast = reshape(shape = var_425, x = attn_weights_19_cast)[name = tensor<string, []>("op_426_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_21_cast = add(x = var_426_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_21_cast")];
            tensor<int32, [3]> var_431 = const()[name = tensor<string, []>("op_431"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_53_cast = reshape(shape = var_431, x = attn_weights_21_cast)[name = tensor<string, []>("input_53_cast")];
            tensor<fp16, [16, 77, 77]> input_55_cast = softmax(axis = var_5, x = input_53_cast)[name = tensor<string, []>("input_55_cast")];
            tensor<bool, []> attn_output_19_transpose_x_0 = const()[name = tensor<string, []>("attn_output_19_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_19_transpose_y_0 = const()[name = tensor<string, []>("attn_output_19_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_19_cast = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = input_55_cast, y = value_states_15_cast)[name = tensor<string, []>("attn_output_19_cast")];
            tensor<int32, [4]> var_436 = const()[name = tensor<string, []>("op_436"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_21_cast = reshape(shape = var_436, x = attn_output_19_cast)[name = tensor<string, []>("attn_output_21_cast")];
            tensor<int32, [4]> attn_output_23_perm_0 = const()[name = tensor<string, []>("attn_output_23_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_439 = const()[name = tensor<string, []>("op_439"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_95 = transpose(perm = attn_output_23_perm_0, x = attn_output_21_cast)[name = tensor<string, []>("transpose_95")];
            tensor<fp16, [1, 77, 1024]> input_57_cast = reshape(shape = var_439, x = transpose_95)[name = tensor<string, []>("input_57_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111504896))), lut = tensor<fp16, [4]>([-0x1.134p-6, -0x1.498p-8, 0x1.488p-8, 0x1.13cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111767104)))];
            tensor<fp16, [1, 77, 1024]> linear_21_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_palettized, x = input_57_cast)[name = tensor<string, []>("linear_21_cast")];
            tensor<fp16, [1, 77, 1024]> input_59_cast = add(x = input_51_cast, y = linear_21_cast)[name = tensor<string, []>("input_59_cast")];
            tensor<int32, [1]> input_61_axes_0 = const()[name = tensor<string, []>("input_61_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111769216)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111771328)))];
            tensor<fp16, [1, 77, 1024]> input_61_cast = layer_norm(axes = input_61_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16, x = input_59_cast)[name = tensor<string, []>("input_61_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(111773440))), lut = tensor<fp16, [4]>([-0x1.474p-6, -0x1.82p-8, 0x1.908p-8, 0x1.4b4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112822080))), lut = tensor<fp16, [4]>([-0x1.97p-2, -0x1.244p-2, -0x1.034p-1, 0x1.3ep-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_22_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16_palettized, x = input_61_cast)[name = tensor<string, []>("linear_22_cast")];
            tensor<string, []> input_65_mode_0 = const()[name = tensor<string, []>("input_65_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_65_cast = gelu(mode = input_65_mode_0, x = linear_22_cast)[name = tensor<string, []>("input_65_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112823168))), lut = tensor<fp16, [4]>([-0x1.134p-6, -0x1.488p-8, 0x1.49cp-8, 0x1.13cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113871808)))];
            tensor<fp16, [1, 77, 1024]> linear_23_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16_palettized, x = input_65_cast)[name = tensor<string, []>("linear_23_cast")];
            tensor<fp16, [1, 77, 1024]> input_67_cast = add(x = input_59_cast, y = linear_23_cast)[name = tensor<string, []>("input_67_cast")];
            tensor<int32, [1]> hidden_states_25_axes_0 = const()[name = tensor<string, []>("hidden_states_25_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113873920)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113876032)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_25_cast = layer_norm(axes = hidden_states_25_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16, x = input_67_cast)[name = tensor<string, []>("hidden_states_25_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(113878144))), lut = tensor<fp16, [4]>([-0x1.5d8p-6, -0x1.a34p-8, 0x1.988p-8, 0x1.5acp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114140352)))];
            tensor<fp16, [1, 77, 1024]> linear_24_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("linear_24_cast")];
            tensor<fp16, []> var_478_to_fp16 = const()[name = tensor<string, []>("op_478_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_29_cast = mul(x = linear_24_cast, y = var_478_to_fp16)[name = tensor<string, []>("tensor_29_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114142464))), lut = tensor<fp16, [4]>([-0x1.5ccp-6, -0x1.9c4p-8, 0x1.9c4p-8, 0x1.5ccp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114404672)))];
            tensor<fp16, [1, 77, 1024]> linear_25_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("linear_25_cast")];
            tensor<int32, [4]> var_483 = const()[name = tensor<string, []>("op_483"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_484_cast = reshape(shape = var_483, x = linear_25_cast)[name = tensor<string, []>("op_484_cast")];
            tensor<int32, [4]> var_485_perm_0 = const()[name = tensor<string, []>("op_485_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114406784))), lut = tensor<fp16, [4]>([-0x1.178p-6, -0x1.4f8p-8, 0x1.4a4p-8, 0x1.16cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114668992)))];
            tensor<fp16, [1, 77, 1024]> linear_26_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_25_cast)[name = tensor<string, []>("linear_26_cast")];
            tensor<int32, [4]> var_490 = const()[name = tensor<string, []>("op_490"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_491_cast = reshape(shape = var_490, x = linear_26_cast)[name = tensor<string, []>("op_491_cast")];
            tensor<int32, [4]> var_492_perm_0 = const()[name = tensor<string, []>("op_492_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_499 = const()[name = tensor<string, []>("op_499"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_500_cast = reshape(shape = var_499, x = tensor_29_cast)[name = tensor<string, []>("op_500_cast")];
            tensor<int32, [4]> var_501_perm_0 = const()[name = tensor<string, []>("op_501_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_503 = const()[name = tensor<string, []>("op_503"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_94 = transpose(perm = var_501_perm_0, x = var_500_cast)[name = tensor<string, []>("transpose_94")];
            tensor<fp16, [16, 77, 64]> query_states_9_cast = reshape(shape = var_503, x = transpose_94)[name = tensor<string, []>("query_states_9_cast")];
            tensor<int32, [3]> var_505 = const()[name = tensor<string, []>("op_505"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_93 = transpose(perm = var_485_perm_0, x = var_484_cast)[name = tensor<string, []>("transpose_93")];
            tensor<fp16, [16, 77, 64]> key_states_19_cast = reshape(shape = var_505, x = transpose_93)[name = tensor<string, []>("key_states_19_cast")];
            tensor<int32, [3]> var_507 = const()[name = tensor<string, []>("op_507"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_92 = transpose(perm = var_492_perm_0, x = var_491_cast)[name = tensor<string, []>("transpose_92")];
            tensor<fp16, [16, 77, 64]> value_states_19_cast = reshape(shape = var_507, x = transpose_92)[name = tensor<string, []>("value_states_19_cast")];
            tensor<int32, [3]> var_510_perm_0 = const()[name = tensor<string, []>("op_510_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_25_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_25_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_91 = transpose(perm = var_510_perm_0, x = key_states_19_cast)[name = tensor<string, []>("transpose_91")];
            tensor<fp16, [16, 77, 77]> attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = query_states_9_cast, y = transpose_91)[name = tensor<string, []>("attn_weights_25_cast")];
            tensor<int32, [4]> var_512 = const()[name = tensor<string, []>("op_512"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_513_cast = reshape(shape = var_512, x = attn_weights_25_cast)[name = tensor<string, []>("op_513_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_27_cast = add(x = var_513_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_27_cast")];
            tensor<int32, [3]> var_518 = const()[name = tensor<string, []>("op_518"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_69_cast = reshape(shape = var_518, x = attn_weights_27_cast)[name = tensor<string, []>("input_69_cast")];
            tensor<fp16, [16, 77, 77]> input_71_cast = softmax(axis = var_5, x = input_69_cast)[name = tensor<string, []>("input_71_cast")];
            tensor<bool, []> attn_output_25_transpose_x_0 = const()[name = tensor<string, []>("attn_output_25_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_25_transpose_y_0 = const()[name = tensor<string, []>("attn_output_25_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_25_cast = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = input_71_cast, y = value_states_19_cast)[name = tensor<string, []>("attn_output_25_cast")];
            tensor<int32, [4]> var_523 = const()[name = tensor<string, []>("op_523"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_27_cast = reshape(shape = var_523, x = attn_output_25_cast)[name = tensor<string, []>("attn_output_27_cast")];
            tensor<int32, [4]> attn_output_29_perm_0 = const()[name = tensor<string, []>("attn_output_29_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_526 = const()[name = tensor<string, []>("op_526"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_90 = transpose(perm = attn_output_29_perm_0, x = attn_output_27_cast)[name = tensor<string, []>("transpose_90")];
            tensor<fp16, [1, 77, 1024]> input_73_cast = reshape(shape = var_526, x = transpose_90)[name = tensor<string, []>("input_73_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114671104))), lut = tensor<fp16, [4]>([-0x1.16p-6, -0x1.494p-8, 0x1.5p-8, 0x1.168p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114933312)))];
            tensor<fp16, [1, 77, 1024]> linear_27_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_palettized, x = input_73_cast)[name = tensor<string, []>("linear_27_cast")];
            tensor<fp16, [1, 77, 1024]> input_75_cast = add(x = input_67_cast, y = linear_27_cast)[name = tensor<string, []>("input_75_cast")];
            tensor<int32, [1]> input_77_axes_0 = const()[name = tensor<string, []>("input_77_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114935424)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114937536)))];
            tensor<fp16, [1, 77, 1024]> input_77_cast = layer_norm(axes = input_77_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16, x = input_75_cast)[name = tensor<string, []>("input_77_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(114939648))), lut = tensor<fp16, [4]>([-0x1.46cp-6, -0x1.83p-8, 0x1.8ccp-8, 0x1.49cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115988288))), lut = tensor<fp16, [4]>([-0x1.914p-2, 0x1.8cp-4, -0x1.fcp-2, -0x1.21p-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_28_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16_palettized, x = input_77_cast)[name = tensor<string, []>("linear_28_cast")];
            tensor<string, []> input_81_mode_0 = const()[name = tensor<string, []>("input_81_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_81_cast = gelu(mode = input_81_mode_0, x = linear_28_cast)[name = tensor<string, []>("input_81_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115989376))), lut = tensor<fp16, [4]>([-0x1.14p-6, -0x1.478p-8, 0x1.4e4p-8, 0x1.158p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117038016)))];
            tensor<fp16, [1, 77, 1024]> linear_29_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16_palettized, x = input_81_cast)[name = tensor<string, []>("linear_29_cast")];
            tensor<fp16, [1, 77, 1024]> input_83_cast = add(x = input_75_cast, y = linear_29_cast)[name = tensor<string, []>("input_83_cast")];
            tensor<int32, [1]> hidden_states_31_axes_0 = const()[name = tensor<string, []>("hidden_states_31_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117040128)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117042240)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_31_cast = layer_norm(axes = hidden_states_31_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16, x = input_83_cast)[name = tensor<string, []>("hidden_states_31_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117044352))), lut = tensor<fp16, [4]>([-0x1.654p-6, -0x1.a84p-8, 0x1.a78p-8, 0x1.64cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117306560)))];
            tensor<fp16, [1, 77, 1024]> linear_30_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_31_cast)[name = tensor<string, []>("linear_30_cast")];
            tensor<fp16, []> var_565_to_fp16 = const()[name = tensor<string, []>("op_565_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_35_cast = mul(x = linear_30_cast, y = var_565_to_fp16)[name = tensor<string, []>("tensor_35_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117308672))), lut = tensor<fp16, [4]>([-0x1.65p-6, -0x1.a68p-8, 0x1.aa8p-8, 0x1.674p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117570880)))];
            tensor<fp16, [1, 77, 1024]> linear_31_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_31_cast)[name = tensor<string, []>("linear_31_cast")];
            tensor<int32, [4]> var_570 = const()[name = tensor<string, []>("op_570"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_571_cast = reshape(shape = var_570, x = linear_31_cast)[name = tensor<string, []>("op_571_cast")];
            tensor<int32, [4]> var_572_perm_0 = const()[name = tensor<string, []>("op_572_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117572992))), lut = tensor<fp16, [4]>([-0x1.168p-6, -0x1.4c4p-8, 0x1.49cp-8, 0x1.15cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117835200)))];
            tensor<fp16, [1, 77, 1024]> linear_32_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_31_cast)[name = tensor<string, []>("linear_32_cast")];
            tensor<int32, [4]> var_577 = const()[name = tensor<string, []>("op_577"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_578_cast = reshape(shape = var_577, x = linear_32_cast)[name = tensor<string, []>("op_578_cast")];
            tensor<int32, [4]> var_579_perm_0 = const()[name = tensor<string, []>("op_579_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_586 = const()[name = tensor<string, []>("op_586"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_587_cast = reshape(shape = var_586, x = tensor_35_cast)[name = tensor<string, []>("op_587_cast")];
            tensor<int32, [4]> var_588_perm_0 = const()[name = tensor<string, []>("op_588_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_590 = const()[name = tensor<string, []>("op_590"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_89 = transpose(perm = var_588_perm_0, x = var_587_cast)[name = tensor<string, []>("transpose_89")];
            tensor<fp16, [16, 77, 64]> query_states_11_cast = reshape(shape = var_590, x = transpose_89)[name = tensor<string, []>("query_states_11_cast")];
            tensor<int32, [3]> var_592 = const()[name = tensor<string, []>("op_592"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_88 = transpose(perm = var_572_perm_0, x = var_571_cast)[name = tensor<string, []>("transpose_88")];
            tensor<fp16, [16, 77, 64]> key_states_23_cast = reshape(shape = var_592, x = transpose_88)[name = tensor<string, []>("key_states_23_cast")];
            tensor<int32, [3]> var_594 = const()[name = tensor<string, []>("op_594"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_87 = transpose(perm = var_579_perm_0, x = var_578_cast)[name = tensor<string, []>("transpose_87")];
            tensor<fp16, [16, 77, 64]> value_states_23_cast = reshape(shape = var_594, x = transpose_87)[name = tensor<string, []>("value_states_23_cast")];
            tensor<int32, [3]> var_597_perm_0 = const()[name = tensor<string, []>("op_597_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_31_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_31_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_31_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_31_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_86 = transpose(perm = var_597_perm_0, x = key_states_23_cast)[name = tensor<string, []>("transpose_86")];
            tensor<fp16, [16, 77, 77]> attn_weights_31_cast = matmul(transpose_x = attn_weights_31_transpose_x_0, transpose_y = attn_weights_31_transpose_y_0, x = query_states_11_cast, y = transpose_86)[name = tensor<string, []>("attn_weights_31_cast")];
            tensor<int32, [4]> var_599 = const()[name = tensor<string, []>("op_599"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_600_cast = reshape(shape = var_599, x = attn_weights_31_cast)[name = tensor<string, []>("op_600_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_33_cast = add(x = var_600_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_33_cast")];
            tensor<int32, [3]> var_605 = const()[name = tensor<string, []>("op_605"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_85_cast = reshape(shape = var_605, x = attn_weights_33_cast)[name = tensor<string, []>("input_85_cast")];
            tensor<fp16, [16, 77, 77]> input_87_cast = softmax(axis = var_5, x = input_85_cast)[name = tensor<string, []>("input_87_cast")];
            tensor<bool, []> attn_output_31_transpose_x_0 = const()[name = tensor<string, []>("attn_output_31_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_31_transpose_y_0 = const()[name = tensor<string, []>("attn_output_31_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_31_cast = matmul(transpose_x = attn_output_31_transpose_x_0, transpose_y = attn_output_31_transpose_y_0, x = input_87_cast, y = value_states_23_cast)[name = tensor<string, []>("attn_output_31_cast")];
            tensor<int32, [4]> var_610 = const()[name = tensor<string, []>("op_610"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_33_cast = reshape(shape = var_610, x = attn_output_31_cast)[name = tensor<string, []>("attn_output_33_cast")];
            tensor<int32, [4]> attn_output_35_perm_0 = const()[name = tensor<string, []>("attn_output_35_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_613 = const()[name = tensor<string, []>("op_613"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_85 = transpose(perm = attn_output_35_perm_0, x = attn_output_33_cast)[name = tensor<string, []>("transpose_85")];
            tensor<fp16, [1, 77, 1024]> input_89_cast = reshape(shape = var_613, x = transpose_85)[name = tensor<string, []>("input_89_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117837312))), lut = tensor<fp16, [4]>([-0x1.15p-6, -0x1.49cp-8, 0x1.4b8p-8, 0x1.15p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118099520)))];
            tensor<fp16, [1, 77, 1024]> linear_33_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_palettized, x = input_89_cast)[name = tensor<string, []>("linear_33_cast")];
            tensor<fp16, [1, 77, 1024]> input_91_cast = add(x = input_83_cast, y = linear_33_cast)[name = tensor<string, []>("input_91_cast")];
            tensor<int32, [1]> input_93_axes_0 = const()[name = tensor<string, []>("input_93_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118101632)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118103744)))];
            tensor<fp16, [1, 77, 1024]> input_93_cast = layer_norm(axes = input_93_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16, x = input_91_cast)[name = tensor<string, []>("input_93_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118105856))), lut = tensor<fp16, [4]>([-0x1.45cp-6, -0x1.804p-8, 0x1.8ep-8, 0x1.4ap-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(119154496))), lut = tensor<fp16, [4]>([-0x1.56cp-4, -0x1.014p-1, -0x1.3a8p-2, -0x1.9dcp-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_34_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16_palettized, x = input_93_cast)[name = tensor<string, []>("linear_34_cast")];
            tensor<string, []> input_97_mode_0 = const()[name = tensor<string, []>("input_97_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_97_cast = gelu(mode = input_97_mode_0, x = linear_34_cast)[name = tensor<string, []>("input_97_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(119155584))), lut = tensor<fp16, [4]>([-0x1.11cp-6, -0x1.45cp-8, 0x1.488p-8, 0x1.124p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120204224)))];
            tensor<fp16, [1, 77, 1024]> linear_35_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16_palettized, x = input_97_cast)[name = tensor<string, []>("linear_35_cast")];
            tensor<fp16, [1, 77, 1024]> input_99_cast = add(x = input_91_cast, y = linear_35_cast)[name = tensor<string, []>("input_99_cast")];
            tensor<int32, [1]> hidden_states_37_axes_0 = const()[name = tensor<string, []>("hidden_states_37_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120206336)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120208448)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_37_cast = layer_norm(axes = hidden_states_37_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16, x = input_99_cast)[name = tensor<string, []>("hidden_states_37_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120210560))), lut = tensor<fp16, [4]>([-0x1.598p-6, -0x1.9a4p-8, 0x1.9e8p-8, 0x1.5a8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120472768)))];
            tensor<fp16, [1, 77, 1024]> linear_36_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_37_cast)[name = tensor<string, []>("linear_36_cast")];
            tensor<fp16, []> var_652_to_fp16 = const()[name = tensor<string, []>("op_652_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_41_cast = mul(x = linear_36_cast, y = var_652_to_fp16)[name = tensor<string, []>("tensor_41_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120474880))), lut = tensor<fp16, [4]>([-0x1.59cp-6, -0x1.9c4p-8, 0x1.99cp-8, 0x1.594p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120737088)))];
            tensor<fp16, [1, 77, 1024]> linear_37_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_37_cast)[name = tensor<string, []>("linear_37_cast")];
            tensor<int32, [4]> var_657 = const()[name = tensor<string, []>("op_657"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_658_cast = reshape(shape = var_657, x = linear_37_cast)[name = tensor<string, []>("op_658_cast")];
            tensor<int32, [4]> var_659_perm_0 = const()[name = tensor<string, []>("op_659_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120739200))), lut = tensor<fp16, [4]>([-0x1.17cp-6, -0x1.4f4p-8, 0x1.4bp-8, 0x1.164p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121001408)))];
            tensor<fp16, [1, 77, 1024]> linear_38_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_37_cast)[name = tensor<string, []>("linear_38_cast")];
            tensor<int32, [4]> var_664 = const()[name = tensor<string, []>("op_664"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_665_cast = reshape(shape = var_664, x = linear_38_cast)[name = tensor<string, []>("op_665_cast")];
            tensor<int32, [4]> var_666_perm_0 = const()[name = tensor<string, []>("op_666_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_673 = const()[name = tensor<string, []>("op_673"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_674_cast = reshape(shape = var_673, x = tensor_41_cast)[name = tensor<string, []>("op_674_cast")];
            tensor<int32, [4]> var_675_perm_0 = const()[name = tensor<string, []>("op_675_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_677 = const()[name = tensor<string, []>("op_677"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_84 = transpose(perm = var_675_perm_0, x = var_674_cast)[name = tensor<string, []>("transpose_84")];
            tensor<fp16, [16, 77, 64]> query_states_13_cast = reshape(shape = var_677, x = transpose_84)[name = tensor<string, []>("query_states_13_cast")];
            tensor<int32, [3]> var_679 = const()[name = tensor<string, []>("op_679"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_83 = transpose(perm = var_659_perm_0, x = var_658_cast)[name = tensor<string, []>("transpose_83")];
            tensor<fp16, [16, 77, 64]> key_states_27_cast = reshape(shape = var_679, x = transpose_83)[name = tensor<string, []>("key_states_27_cast")];
            tensor<int32, [3]> var_681 = const()[name = tensor<string, []>("op_681"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_82 = transpose(perm = var_666_perm_0, x = var_665_cast)[name = tensor<string, []>("transpose_82")];
            tensor<fp16, [16, 77, 64]> value_states_27_cast = reshape(shape = var_681, x = transpose_82)[name = tensor<string, []>("value_states_27_cast")];
            tensor<int32, [3]> var_684_perm_0 = const()[name = tensor<string, []>("op_684_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_37_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_37_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_81 = transpose(perm = var_684_perm_0, x = key_states_27_cast)[name = tensor<string, []>("transpose_81")];
            tensor<fp16, [16, 77, 77]> attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = query_states_13_cast, y = transpose_81)[name = tensor<string, []>("attn_weights_37_cast")];
            tensor<int32, [4]> var_686 = const()[name = tensor<string, []>("op_686"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_687_cast = reshape(shape = var_686, x = attn_weights_37_cast)[name = tensor<string, []>("op_687_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_39_cast = add(x = var_687_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_39_cast")];
            tensor<int32, [3]> var_692 = const()[name = tensor<string, []>("op_692"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_101_cast = reshape(shape = var_692, x = attn_weights_39_cast)[name = tensor<string, []>("input_101_cast")];
            tensor<fp16, [16, 77, 77]> input_103_cast = softmax(axis = var_5, x = input_101_cast)[name = tensor<string, []>("input_103_cast")];
            tensor<bool, []> attn_output_37_transpose_x_0 = const()[name = tensor<string, []>("attn_output_37_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_37_transpose_y_0 = const()[name = tensor<string, []>("attn_output_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_37_cast = matmul(transpose_x = attn_output_37_transpose_x_0, transpose_y = attn_output_37_transpose_y_0, x = input_103_cast, y = value_states_27_cast)[name = tensor<string, []>("attn_output_37_cast")];
            tensor<int32, [4]> var_697 = const()[name = tensor<string, []>("op_697"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_39_cast = reshape(shape = var_697, x = attn_output_37_cast)[name = tensor<string, []>("attn_output_39_cast")];
            tensor<int32, [4]> attn_output_41_perm_0 = const()[name = tensor<string, []>("attn_output_41_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_700 = const()[name = tensor<string, []>("op_700"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_80 = transpose(perm = attn_output_41_perm_0, x = attn_output_39_cast)[name = tensor<string, []>("transpose_80")];
            tensor<fp16, [1, 77, 1024]> input_105_cast = reshape(shape = var_700, x = transpose_80)[name = tensor<string, []>("input_105_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121003520))), lut = tensor<fp16, [4]>([-0x1.16cp-6, -0x1.4ccp-8, 0x1.4dcp-8, 0x1.17p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121265728)))];
            tensor<fp16, [1, 77, 1024]> linear_39_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_palettized, x = input_105_cast)[name = tensor<string, []>("linear_39_cast")];
            tensor<fp16, [1, 77, 1024]> input_107_cast = add(x = input_99_cast, y = linear_39_cast)[name = tensor<string, []>("input_107_cast")];
            tensor<int32, [1]> input_109_axes_0 = const()[name = tensor<string, []>("input_109_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121267840)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121269952)))];
            tensor<fp16, [1, 77, 1024]> input_109_cast = layer_norm(axes = input_109_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16, x = input_107_cast)[name = tensor<string, []>("input_109_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121272064))), lut = tensor<fp16, [4]>([-0x1.45p-6, -0x1.824p-8, 0x1.87cp-8, 0x1.47p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122320704))), lut = tensor<fp16, [4]>([-0x1.46p-2, -0x1.accp-2, 0x1.3p-6, -0x1.08cp-1]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_40_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16_palettized, x = input_109_cast)[name = tensor<string, []>("linear_40_cast")];
            tensor<string, []> input_113_mode_0 = const()[name = tensor<string, []>("input_113_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_113_cast = gelu(mode = input_113_mode_0, x = linear_40_cast)[name = tensor<string, []>("input_113_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(122321792))), lut = tensor<fp16, [4]>([-0x1.0fcp-6, -0x1.444p-8, 0x1.46p-8, 0x1.108p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123370432)))];
            tensor<fp16, [1, 77, 1024]> linear_41_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16_palettized, x = input_113_cast)[name = tensor<string, []>("linear_41_cast")];
            tensor<fp16, [1, 77, 1024]> input_115_cast = add(x = input_107_cast, y = linear_41_cast)[name = tensor<string, []>("input_115_cast")];
            tensor<int32, [1]> hidden_states_43_axes_0 = const()[name = tensor<string, []>("hidden_states_43_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123372544)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123374656)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_43_cast = layer_norm(axes = hidden_states_43_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16, x = input_115_cast)[name = tensor<string, []>("hidden_states_43_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123376768))), lut = tensor<fp16, [4]>([-0x1.588p-6, -0x1.988p-8, 0x1.9acp-8, 0x1.594p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123638976)))];
            tensor<fp16, [1, 77, 1024]> linear_42_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("linear_42_cast")];
            tensor<fp16, []> var_739_to_fp16 = const()[name = tensor<string, []>("op_739_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_47_cast = mul(x = linear_42_cast, y = var_739_to_fp16)[name = tensor<string, []>("tensor_47_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123641088))), lut = tensor<fp16, [4]>([-0x1.558p-6, -0x1.958p-8, 0x1.978p-8, 0x1.56cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123903296)))];
            tensor<fp16, [1, 77, 1024]> linear_43_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("linear_43_cast")];
            tensor<int32, [4]> var_744 = const()[name = tensor<string, []>("op_744"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_745_cast = reshape(shape = var_744, x = linear_43_cast)[name = tensor<string, []>("op_745_cast")];
            tensor<int32, [4]> var_746_perm_0 = const()[name = tensor<string, []>("op_746_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123905408))), lut = tensor<fp16, [4]>([-0x1.1bp-6, -0x1.51p-8, 0x1.548p-8, 0x1.1c8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(124167616)))];
            tensor<fp16, [1, 77, 1024]> linear_44_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_43_cast)[name = tensor<string, []>("linear_44_cast")];
            tensor<int32, [4]> var_751 = const()[name = tensor<string, []>("op_751"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_752_cast = reshape(shape = var_751, x = linear_44_cast)[name = tensor<string, []>("op_752_cast")];
            tensor<int32, [4]> var_753_perm_0 = const()[name = tensor<string, []>("op_753_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_760 = const()[name = tensor<string, []>("op_760"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_761_cast = reshape(shape = var_760, x = tensor_47_cast)[name = tensor<string, []>("op_761_cast")];
            tensor<int32, [4]> var_762_perm_0 = const()[name = tensor<string, []>("op_762_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_764 = const()[name = tensor<string, []>("op_764"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_79 = transpose(perm = var_762_perm_0, x = var_761_cast)[name = tensor<string, []>("transpose_79")];
            tensor<fp16, [16, 77, 64]> query_states_15_cast = reshape(shape = var_764, x = transpose_79)[name = tensor<string, []>("query_states_15_cast")];
            tensor<int32, [3]> var_766 = const()[name = tensor<string, []>("op_766"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_78 = transpose(perm = var_746_perm_0, x = var_745_cast)[name = tensor<string, []>("transpose_78")];
            tensor<fp16, [16, 77, 64]> key_states_31_cast = reshape(shape = var_766, x = transpose_78)[name = tensor<string, []>("key_states_31_cast")];
            tensor<int32, [3]> var_768 = const()[name = tensor<string, []>("op_768"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_77 = transpose(perm = var_753_perm_0, x = var_752_cast)[name = tensor<string, []>("transpose_77")];
            tensor<fp16, [16, 77, 64]> value_states_31_cast = reshape(shape = var_768, x = transpose_77)[name = tensor<string, []>("value_states_31_cast")];
            tensor<int32, [3]> var_771_perm_0 = const()[name = tensor<string, []>("op_771_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_43_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_43_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_43_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_43_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_76 = transpose(perm = var_771_perm_0, x = key_states_31_cast)[name = tensor<string, []>("transpose_76")];
            tensor<fp16, [16, 77, 77]> attn_weights_43_cast = matmul(transpose_x = attn_weights_43_transpose_x_0, transpose_y = attn_weights_43_transpose_y_0, x = query_states_15_cast, y = transpose_76)[name = tensor<string, []>("attn_weights_43_cast")];
            tensor<int32, [4]> var_773 = const()[name = tensor<string, []>("op_773"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_774_cast = reshape(shape = var_773, x = attn_weights_43_cast)[name = tensor<string, []>("op_774_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_45_cast = add(x = var_774_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_45_cast")];
            tensor<int32, [3]> var_779 = const()[name = tensor<string, []>("op_779"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_117_cast = reshape(shape = var_779, x = attn_weights_45_cast)[name = tensor<string, []>("input_117_cast")];
            tensor<fp16, [16, 77, 77]> input_119_cast = softmax(axis = var_5, x = input_117_cast)[name = tensor<string, []>("input_119_cast")];
            tensor<bool, []> attn_output_43_transpose_x_0 = const()[name = tensor<string, []>("attn_output_43_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_43_transpose_y_0 = const()[name = tensor<string, []>("attn_output_43_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_43_cast = matmul(transpose_x = attn_output_43_transpose_x_0, transpose_y = attn_output_43_transpose_y_0, x = input_119_cast, y = value_states_31_cast)[name = tensor<string, []>("attn_output_43_cast")];
            tensor<int32, [4]> var_784 = const()[name = tensor<string, []>("op_784"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_45_cast = reshape(shape = var_784, x = attn_output_43_cast)[name = tensor<string, []>("attn_output_45_cast")];
            tensor<int32, [4]> attn_output_47_perm_0 = const()[name = tensor<string, []>("attn_output_47_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_787 = const()[name = tensor<string, []>("op_787"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_75 = transpose(perm = attn_output_47_perm_0, x = attn_output_45_cast)[name = tensor<string, []>("transpose_75")];
            tensor<fp16, [1, 77, 1024]> input_121_cast = reshape(shape = var_787, x = transpose_75)[name = tensor<string, []>("input_121_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(124169728))), lut = tensor<fp16, [4]>([-0x1.198p-6, -0x1.508p-8, 0x1.514p-8, 0x1.1ap-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(124431936)))];
            tensor<fp16, [1, 77, 1024]> linear_45_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_palettized, x = input_121_cast)[name = tensor<string, []>("linear_45_cast")];
            tensor<fp16, [1, 77, 1024]> input_123_cast = add(x = input_115_cast, y = linear_45_cast)[name = tensor<string, []>("input_123_cast")];
            tensor<int32, [1]> input_125_axes_0 = const()[name = tensor<string, []>("input_125_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(124434048)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(124436160)))];
            tensor<fp16, [1, 77, 1024]> input_125_cast = layer_norm(axes = input_125_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16, x = input_123_cast)[name = tensor<string, []>("input_125_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(124438272))), lut = tensor<fp16, [4]>([-0x1.444p-6, -0x1.838p-8, 0x1.82p-8, 0x1.44p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125486912))), lut = tensor<fp16, [4]>([-0x1.9acp-2, -0x1.294p-2, 0x1.f5cp-3, -0x1.02p-1]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_46_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16_palettized, x = input_125_cast)[name = tensor<string, []>("linear_46_cast")];
            tensor<string, []> input_129_mode_0 = const()[name = tensor<string, []>("input_129_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_129_cast = gelu(mode = input_129_mode_0, x = linear_46_cast)[name = tensor<string, []>("input_129_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125488000))), lut = tensor<fp16, [4]>([-0x1.0ccp-6, -0x1.408p-8, 0x1.424p-8, 0x1.0d8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126536640)))];
            tensor<fp16, [1, 77, 1024]> linear_47_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16_palettized, x = input_129_cast)[name = tensor<string, []>("linear_47_cast")];
            tensor<fp16, [1, 77, 1024]> input_131_cast = add(x = input_123_cast, y = linear_47_cast)[name = tensor<string, []>("input_131_cast")];
            tensor<int32, [1]> hidden_states_49_axes_0 = const()[name = tensor<string, []>("hidden_states_49_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126538752)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126540864)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_49_cast = layer_norm(axes = hidden_states_49_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16, x = input_131_cast)[name = tensor<string, []>("hidden_states_49_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126542976))), lut = tensor<fp16, [4]>([-0x1.58cp-6, -0x1.9c4p-8, 0x1.9d4p-8, 0x1.59cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126805184)))];
            tensor<fp16, [1, 77, 1024]> linear_48_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("linear_48_cast")];
            tensor<fp16, []> var_826_to_fp16 = const()[name = tensor<string, []>("op_826_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_53_cast = mul(x = linear_48_cast, y = var_826_to_fp16)[name = tensor<string, []>("tensor_53_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126807296))), lut = tensor<fp16, [4]>([-0x1.554p-6, -0x1.94p-8, 0x1.9bcp-8, 0x1.57cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127069504)))];
            tensor<fp16, [1, 77, 1024]> linear_49_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("linear_49_cast")];
            tensor<int32, [4]> var_831 = const()[name = tensor<string, []>("op_831"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_832_cast = reshape(shape = var_831, x = linear_49_cast)[name = tensor<string, []>("op_832_cast")];
            tensor<int32, [4]> var_833_perm_0 = const()[name = tensor<string, []>("op_833_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127071616))), lut = tensor<fp16, [4]>([-0x1.228p-6, -0x1.594p-8, 0x1.5c4p-8, 0x1.23p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127333824)))];
            tensor<fp16, [1, 77, 1024]> linear_50_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_49_cast)[name = tensor<string, []>("linear_50_cast")];
            tensor<int32, [4]> var_838 = const()[name = tensor<string, []>("op_838"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_839_cast = reshape(shape = var_838, x = linear_50_cast)[name = tensor<string, []>("op_839_cast")];
            tensor<int32, [4]> var_840_perm_0 = const()[name = tensor<string, []>("op_840_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_847 = const()[name = tensor<string, []>("op_847"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_848_cast = reshape(shape = var_847, x = tensor_53_cast)[name = tensor<string, []>("op_848_cast")];
            tensor<int32, [4]> var_849_perm_0 = const()[name = tensor<string, []>("op_849_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_851 = const()[name = tensor<string, []>("op_851"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_74 = transpose(perm = var_849_perm_0, x = var_848_cast)[name = tensor<string, []>("transpose_74")];
            tensor<fp16, [16, 77, 64]> query_states_17_cast = reshape(shape = var_851, x = transpose_74)[name = tensor<string, []>("query_states_17_cast")];
            tensor<int32, [3]> var_853 = const()[name = tensor<string, []>("op_853"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_73 = transpose(perm = var_833_perm_0, x = var_832_cast)[name = tensor<string, []>("transpose_73")];
            tensor<fp16, [16, 77, 64]> key_states_35_cast = reshape(shape = var_853, x = transpose_73)[name = tensor<string, []>("key_states_35_cast")];
            tensor<int32, [3]> var_855 = const()[name = tensor<string, []>("op_855"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_72 = transpose(perm = var_840_perm_0, x = var_839_cast)[name = tensor<string, []>("transpose_72")];
            tensor<fp16, [16, 77, 64]> value_states_35_cast = reshape(shape = var_855, x = transpose_72)[name = tensor<string, []>("value_states_35_cast")];
            tensor<int32, [3]> var_858_perm_0 = const()[name = tensor<string, []>("op_858_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_49_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_49_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_71 = transpose(perm = var_858_perm_0, x = key_states_35_cast)[name = tensor<string, []>("transpose_71")];
            tensor<fp16, [16, 77, 77]> attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = query_states_17_cast, y = transpose_71)[name = tensor<string, []>("attn_weights_49_cast")];
            tensor<int32, [4]> var_860 = const()[name = tensor<string, []>("op_860"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_861_cast = reshape(shape = var_860, x = attn_weights_49_cast)[name = tensor<string, []>("op_861_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_51_cast = add(x = var_861_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_51_cast")];
            tensor<int32, [3]> var_866 = const()[name = tensor<string, []>("op_866"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_133_cast = reshape(shape = var_866, x = attn_weights_51_cast)[name = tensor<string, []>("input_133_cast")];
            tensor<fp16, [16, 77, 77]> input_135_cast = softmax(axis = var_5, x = input_133_cast)[name = tensor<string, []>("input_135_cast")];
            tensor<bool, []> attn_output_49_transpose_x_0 = const()[name = tensor<string, []>("attn_output_49_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_49_transpose_y_0 = const()[name = tensor<string, []>("attn_output_49_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_49_cast = matmul(transpose_x = attn_output_49_transpose_x_0, transpose_y = attn_output_49_transpose_y_0, x = input_135_cast, y = value_states_35_cast)[name = tensor<string, []>("attn_output_49_cast")];
            tensor<int32, [4]> var_871 = const()[name = tensor<string, []>("op_871"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_51_cast = reshape(shape = var_871, x = attn_output_49_cast)[name = tensor<string, []>("attn_output_51_cast")];
            tensor<int32, [4]> attn_output_53_perm_0 = const()[name = tensor<string, []>("attn_output_53_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_874 = const()[name = tensor<string, []>("op_874"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_70 = transpose(perm = attn_output_53_perm_0, x = attn_output_51_cast)[name = tensor<string, []>("transpose_70")];
            tensor<fp16, [1, 77, 1024]> input_137_cast = reshape(shape = var_874, x = transpose_70)[name = tensor<string, []>("input_137_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127335936))), lut = tensor<fp16, [4]>([-0x1.21p-6, -0x1.59p-8, 0x1.5bp-8, 0x1.214p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127598144)))];
            tensor<fp16, [1, 77, 1024]> linear_51_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16_palettized, x = input_137_cast)[name = tensor<string, []>("linear_51_cast")];
            tensor<fp16, [1, 77, 1024]> input_139_cast = add(x = input_131_cast, y = linear_51_cast)[name = tensor<string, []>("input_139_cast")];
            tensor<int32, [1]> input_141_axes_0 = const()[name = tensor<string, []>("input_141_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127600256)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127602368)))];
            tensor<fp16, [1, 77, 1024]> input_141_cast = layer_norm(axes = input_141_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16, x = input_139_cast)[name = tensor<string, []>("input_141_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(127604480))), lut = tensor<fp16, [4]>([-0x1.42p-6, -0x1.804p-8, 0x1.81p-8, 0x1.42cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128653120))), lut = tensor<fp16, [4]>([-0x1.3dp-2, -0x1.accp-2, 0x1.114p-3, -0x1.0b4p-1]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_52_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16_palettized, x = input_141_cast)[name = tensor<string, []>("linear_52_cast")];
            tensor<string, []> input_145_mode_0 = const()[name = tensor<string, []>("input_145_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_145_cast = gelu(mode = input_145_mode_0, x = linear_52_cast)[name = tensor<string, []>("input_145_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128654208))), lut = tensor<fp16, [4]>([-0x1.0cp-6, -0x1.408p-8, 0x1.3f4p-8, 0x1.0cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129702848)))];
            tensor<fp16, [1, 77, 1024]> linear_53_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16_palettized, x = input_145_cast)[name = tensor<string, []>("linear_53_cast")];
            tensor<fp16, [1, 77, 1024]> input_147_cast = add(x = input_139_cast, y = linear_53_cast)[name = tensor<string, []>("input_147_cast")];
            tensor<int32, [1]> hidden_states_55_axes_0 = const()[name = tensor<string, []>("hidden_states_55_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129704960)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129707072)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_55_cast = layer_norm(axes = hidden_states_55_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16, x = input_147_cast)[name = tensor<string, []>("hidden_states_55_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129709184))), lut = tensor<fp16, [4]>([-0x1.5c4p-6, -0x1.9fcp-8, 0x1.9dcp-8, 0x1.5b4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129971392)))];
            tensor<fp16, [1, 77, 1024]> linear_54_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_55_cast)[name = tensor<string, []>("linear_54_cast")];
            tensor<fp16, []> var_913_to_fp16 = const()[name = tensor<string, []>("op_913_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_59_cast = mul(x = linear_54_cast, y = var_913_to_fp16)[name = tensor<string, []>("tensor_59_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129973504))), lut = tensor<fp16, [4]>([-0x1.59cp-6, -0x1.9ccp-8, 0x1.9ap-8, 0x1.59p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130235712)))];
            tensor<fp16, [1, 77, 1024]> linear_55_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_55_cast)[name = tensor<string, []>("linear_55_cast")];
            tensor<int32, [4]> var_918 = const()[name = tensor<string, []>("op_918"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_919_cast = reshape(shape = var_918, x = linear_55_cast)[name = tensor<string, []>("op_919_cast")];
            tensor<int32, [4]> var_920_perm_0 = const()[name = tensor<string, []>("op_920_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130237824))), lut = tensor<fp16, [4]>([-0x1.1e8p-6, -0x1.578p-8, 0x1.52cp-8, 0x1.1d8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130500032)))];
            tensor<fp16, [1, 77, 1024]> linear_56_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_55_cast)[name = tensor<string, []>("linear_56_cast")];
            tensor<int32, [4]> var_925 = const()[name = tensor<string, []>("op_925"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_926_cast = reshape(shape = var_925, x = linear_56_cast)[name = tensor<string, []>("op_926_cast")];
            tensor<int32, [4]> var_927_perm_0 = const()[name = tensor<string, []>("op_927_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_934 = const()[name = tensor<string, []>("op_934"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_935_cast = reshape(shape = var_934, x = tensor_59_cast)[name = tensor<string, []>("op_935_cast")];
            tensor<int32, [4]> var_936_perm_0 = const()[name = tensor<string, []>("op_936_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_938 = const()[name = tensor<string, []>("op_938"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_69 = transpose(perm = var_936_perm_0, x = var_935_cast)[name = tensor<string, []>("transpose_69")];
            tensor<fp16, [16, 77, 64]> query_states_19_cast = reshape(shape = var_938, x = transpose_69)[name = tensor<string, []>("query_states_19_cast")];
            tensor<int32, [3]> var_940 = const()[name = tensor<string, []>("op_940"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_68 = transpose(perm = var_920_perm_0, x = var_919_cast)[name = tensor<string, []>("transpose_68")];
            tensor<fp16, [16, 77, 64]> key_states_39_cast = reshape(shape = var_940, x = transpose_68)[name = tensor<string, []>("key_states_39_cast")];
            tensor<int32, [3]> var_942 = const()[name = tensor<string, []>("op_942"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_67 = transpose(perm = var_927_perm_0, x = var_926_cast)[name = tensor<string, []>("transpose_67")];
            tensor<fp16, [16, 77, 64]> value_states_39_cast = reshape(shape = var_942, x = transpose_67)[name = tensor<string, []>("value_states_39_cast")];
            tensor<int32, [3]> var_945_perm_0 = const()[name = tensor<string, []>("op_945_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_55_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_55_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_55_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_55_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_66 = transpose(perm = var_945_perm_0, x = key_states_39_cast)[name = tensor<string, []>("transpose_66")];
            tensor<fp16, [16, 77, 77]> attn_weights_55_cast = matmul(transpose_x = attn_weights_55_transpose_x_0, transpose_y = attn_weights_55_transpose_y_0, x = query_states_19_cast, y = transpose_66)[name = tensor<string, []>("attn_weights_55_cast")];
            tensor<int32, [4]> var_947 = const()[name = tensor<string, []>("op_947"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_948_cast = reshape(shape = var_947, x = attn_weights_55_cast)[name = tensor<string, []>("op_948_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_57_cast = add(x = var_948_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_57_cast")];
            tensor<int32, [3]> var_953 = const()[name = tensor<string, []>("op_953"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_149_cast = reshape(shape = var_953, x = attn_weights_57_cast)[name = tensor<string, []>("input_149_cast")];
            tensor<fp16, [16, 77, 77]> input_151_cast = softmax(axis = var_5, x = input_149_cast)[name = tensor<string, []>("input_151_cast")];
            tensor<bool, []> attn_output_55_transpose_x_0 = const()[name = tensor<string, []>("attn_output_55_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_55_transpose_y_0 = const()[name = tensor<string, []>("attn_output_55_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_55_cast = matmul(transpose_x = attn_output_55_transpose_x_0, transpose_y = attn_output_55_transpose_y_0, x = input_151_cast, y = value_states_39_cast)[name = tensor<string, []>("attn_output_55_cast")];
            tensor<int32, [4]> var_958 = const()[name = tensor<string, []>("op_958"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_57_cast = reshape(shape = var_958, x = attn_output_55_cast)[name = tensor<string, []>("attn_output_57_cast")];
            tensor<int32, [4]> attn_output_59_perm_0 = const()[name = tensor<string, []>("attn_output_59_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_961 = const()[name = tensor<string, []>("op_961"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_65 = transpose(perm = attn_output_59_perm_0, x = attn_output_57_cast)[name = tensor<string, []>("transpose_65")];
            tensor<fp16, [1, 77, 1024]> input_153_cast = reshape(shape = var_961, x = transpose_65)[name = tensor<string, []>("input_153_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130502144))), lut = tensor<fp16, [4]>([-0x1.1cp-6, -0x1.534p-8, 0x1.54cp-8, 0x1.1ccp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130764352)))];
            tensor<fp16, [1, 77, 1024]> linear_57_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16_palettized, x = input_153_cast)[name = tensor<string, []>("linear_57_cast")];
            tensor<fp16, [1, 77, 1024]> input_155_cast = add(x = input_147_cast, y = linear_57_cast)[name = tensor<string, []>("input_155_cast")];
            tensor<int32, [1]> input_157_axes_0 = const()[name = tensor<string, []>("input_157_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130766464)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130768576)))];
            tensor<fp16, [1, 77, 1024]> input_157_cast = layer_norm(axes = input_157_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16, x = input_155_cast)[name = tensor<string, []>("input_157_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130770688))), lut = tensor<fp16, [4]>([-0x1.4p-6, -0x1.7b8p-8, 0x1.84p-8, 0x1.42cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131819328))), lut = tensor<fp16, [4]>([-0x1.a08p-2, 0x1.acp-6, -0x1.044p-1, -0x1.38p-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_58_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16_palettized, x = input_157_cast)[name = tensor<string, []>("linear_58_cast")];
            tensor<string, []> input_161_mode_0 = const()[name = tensor<string, []>("input_161_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_161_cast = gelu(mode = input_161_mode_0, x = linear_58_cast)[name = tensor<string, []>("input_161_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131820416))), lut = tensor<fp16, [4]>([-0x1.0a8p-6, -0x1.3e4p-8, 0x1.3f4p-8, 0x1.0acp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132869056)))];
            tensor<fp16, [1, 77, 1024]> linear_59_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16_palettized, x = input_161_cast)[name = tensor<string, []>("linear_59_cast")];
            tensor<fp16, [1, 77, 1024]> input_163_cast = add(x = input_155_cast, y = linear_59_cast)[name = tensor<string, []>("input_163_cast")];
            tensor<int32, [1]> hidden_states_61_axes_0 = const()[name = tensor<string, []>("hidden_states_61_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132871168)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132873280)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_61_cast = layer_norm(axes = hidden_states_61_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16, x = input_163_cast)[name = tensor<string, []>("hidden_states_61_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132875392))), lut = tensor<fp16, [4]>([-0x1.5a8p-6, -0x1.9ecp-8, 0x1.9c4p-8, 0x1.59cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133137600)))];
            tensor<fp16, [1, 77, 1024]> linear_60_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_61_cast)[name = tensor<string, []>("linear_60_cast")];
            tensor<fp16, []> var_1000_to_fp16 = const()[name = tensor<string, []>("op_1000_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_65_cast = mul(x = linear_60_cast, y = var_1000_to_fp16)[name = tensor<string, []>("tensor_65_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133139712))), lut = tensor<fp16, [4]>([-0x1.5ap-6, -0x1.998p-8, 0x1.a04p-8, 0x1.5b8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133401920)))];
            tensor<fp16, [1, 77, 1024]> linear_61_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_61_cast)[name = tensor<string, []>("linear_61_cast")];
            tensor<int32, [4]> var_1005 = const()[name = tensor<string, []>("op_1005"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1006_cast = reshape(shape = var_1005, x = linear_61_cast)[name = tensor<string, []>("op_1006_cast")];
            tensor<int32, [4]> var_1007_perm_0 = const()[name = tensor<string, []>("op_1007_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133404032))), lut = tensor<fp16, [4]>([-0x1.1f8p-6, -0x1.568p-8, 0x1.584p-8, 0x1.208p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133666240)))];
            tensor<fp16, [1, 77, 1024]> linear_62_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_61_cast)[name = tensor<string, []>("linear_62_cast")];
            tensor<int32, [4]> var_1012 = const()[name = tensor<string, []>("op_1012"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1013_cast = reshape(shape = var_1012, x = linear_62_cast)[name = tensor<string, []>("op_1013_cast")];
            tensor<int32, [4]> var_1014_perm_0 = const()[name = tensor<string, []>("op_1014_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1021 = const()[name = tensor<string, []>("op_1021"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1022_cast = reshape(shape = var_1021, x = tensor_65_cast)[name = tensor<string, []>("op_1022_cast")];
            tensor<int32, [4]> var_1023_perm_0 = const()[name = tensor<string, []>("op_1023_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1025 = const()[name = tensor<string, []>("op_1025"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_64 = transpose(perm = var_1023_perm_0, x = var_1022_cast)[name = tensor<string, []>("transpose_64")];
            tensor<fp16, [16, 77, 64]> query_states_21_cast = reshape(shape = var_1025, x = transpose_64)[name = tensor<string, []>("query_states_21_cast")];
            tensor<int32, [3]> var_1027 = const()[name = tensor<string, []>("op_1027"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_63 = transpose(perm = var_1007_perm_0, x = var_1006_cast)[name = tensor<string, []>("transpose_63")];
            tensor<fp16, [16, 77, 64]> key_states_43_cast = reshape(shape = var_1027, x = transpose_63)[name = tensor<string, []>("key_states_43_cast")];
            tensor<int32, [3]> var_1029 = const()[name = tensor<string, []>("op_1029"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_62 = transpose(perm = var_1014_perm_0, x = var_1013_cast)[name = tensor<string, []>("transpose_62")];
            tensor<fp16, [16, 77, 64]> value_states_43_cast = reshape(shape = var_1029, x = transpose_62)[name = tensor<string, []>("value_states_43_cast")];
            tensor<int32, [3]> var_1032_perm_0 = const()[name = tensor<string, []>("op_1032_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_61_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_61_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_61_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_61_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_61 = transpose(perm = var_1032_perm_0, x = key_states_43_cast)[name = tensor<string, []>("transpose_61")];
            tensor<fp16, [16, 77, 77]> attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = query_states_21_cast, y = transpose_61)[name = tensor<string, []>("attn_weights_61_cast")];
            tensor<int32, [4]> var_1034 = const()[name = tensor<string, []>("op_1034"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1035_cast = reshape(shape = var_1034, x = attn_weights_61_cast)[name = tensor<string, []>("op_1035_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_63_cast = add(x = var_1035_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_63_cast")];
            tensor<int32, [3]> var_1040 = const()[name = tensor<string, []>("op_1040"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_165_cast = reshape(shape = var_1040, x = attn_weights_63_cast)[name = tensor<string, []>("input_165_cast")];
            tensor<fp16, [16, 77, 77]> input_167_cast = softmax(axis = var_5, x = input_165_cast)[name = tensor<string, []>("input_167_cast")];
            tensor<bool, []> attn_output_61_transpose_x_0 = const()[name = tensor<string, []>("attn_output_61_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_61_transpose_y_0 = const()[name = tensor<string, []>("attn_output_61_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_61_cast = matmul(transpose_x = attn_output_61_transpose_x_0, transpose_y = attn_output_61_transpose_y_0, x = input_167_cast, y = value_states_43_cast)[name = tensor<string, []>("attn_output_61_cast")];
            tensor<int32, [4]> var_1045 = const()[name = tensor<string, []>("op_1045"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_63_cast = reshape(shape = var_1045, x = attn_output_61_cast)[name = tensor<string, []>("attn_output_63_cast")];
            tensor<int32, [4]> attn_output_65_perm_0 = const()[name = tensor<string, []>("attn_output_65_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1048 = const()[name = tensor<string, []>("op_1048"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_60 = transpose(perm = attn_output_65_perm_0, x = attn_output_63_cast)[name = tensor<string, []>("transpose_60")];
            tensor<fp16, [1, 77, 1024]> input_169_cast = reshape(shape = var_1048, x = transpose_60)[name = tensor<string, []>("input_169_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133668352))), lut = tensor<fp16, [4]>([-0x1.1d4p-6, -0x1.548p-8, 0x1.574p-8, 0x1.1d4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133930560)))];
            tensor<fp16, [1, 77, 1024]> linear_63_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16_palettized, x = input_169_cast)[name = tensor<string, []>("linear_63_cast")];
            tensor<fp16, [1, 77, 1024]> input_171_cast = add(x = input_163_cast, y = linear_63_cast)[name = tensor<string, []>("input_171_cast")];
            tensor<int32, [1]> input_173_axes_0 = const()[name = tensor<string, []>("input_173_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133932672)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133934784)))];
            tensor<fp16, [1, 77, 1024]> input_173_cast = layer_norm(axes = input_173_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16, x = input_171_cast)[name = tensor<string, []>("input_173_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133936896))), lut = tensor<fp16, [4]>([-0x1.4p-6, -0x1.7bcp-8, 0x1.834p-8, 0x1.42p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134985536))), lut = tensor<fp16, [4]>([-0x1.a1cp-2, 0x1.eep-5, -0x1.04cp-1, -0x1.3bcp-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_64_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16_palettized, x = input_173_cast)[name = tensor<string, []>("linear_64_cast")];
            tensor<string, []> input_177_mode_0 = const()[name = tensor<string, []>("input_177_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_177_cast = gelu(mode = input_177_mode_0, x = linear_64_cast)[name = tensor<string, []>("input_177_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134986624))), lut = tensor<fp16, [4]>([-0x1.0ccp-6, -0x1.42p-8, 0x1.41p-8, 0x1.0ccp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136035264)))];
            tensor<fp16, [1, 77, 1024]> linear_65_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16_palettized, x = input_177_cast)[name = tensor<string, []>("linear_65_cast")];
            tensor<fp16, [1, 77, 1024]> input_179_cast = add(x = input_171_cast, y = linear_65_cast)[name = tensor<string, []>("input_179_cast")];
            tensor<int32, [1]> hidden_states_67_axes_0 = const()[name = tensor<string, []>("hidden_states_67_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136037376)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136039488)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_67_cast = layer_norm(axes = hidden_states_67_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16, x = input_179_cast)[name = tensor<string, []>("hidden_states_67_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136041600))), lut = tensor<fp16, [4]>([-0x1.5a8p-6, -0x1.98cp-8, 0x1.a54p-8, 0x1.5c4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136303808)))];
            tensor<fp16, [1, 77, 1024]> linear_66_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("linear_66_cast")];
            tensor<fp16, []> var_1087_to_fp16 = const()[name = tensor<string, []>("op_1087_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_71_cast = mul(x = linear_66_cast, y = var_1087_to_fp16)[name = tensor<string, []>("tensor_71_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136305920))), lut = tensor<fp16, [4]>([-0x1.5bcp-6, -0x1.a1p-8, 0x1.9c8p-8, 0x1.5b4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136568128)))];
            tensor<fp16, [1, 77, 1024]> linear_67_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("linear_67_cast")];
            tensor<int32, [4]> var_1092 = const()[name = tensor<string, []>("op_1092"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1093_cast = reshape(shape = var_1092, x = linear_67_cast)[name = tensor<string, []>("op_1093_cast")];
            tensor<int32, [4]> var_1094_perm_0 = const()[name = tensor<string, []>("op_1094_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136570240))), lut = tensor<fp16, [4]>([-0x1.1e8p-6, -0x1.558p-8, 0x1.55cp-8, 0x1.1ecp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136832448)))];
            tensor<fp16, [1, 77, 1024]> linear_68_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_67_cast)[name = tensor<string, []>("linear_68_cast")];
            tensor<int32, [4]> var_1099 = const()[name = tensor<string, []>("op_1099"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1100_cast = reshape(shape = var_1099, x = linear_68_cast)[name = tensor<string, []>("op_1100_cast")];
            tensor<int32, [4]> var_1101_perm_0 = const()[name = tensor<string, []>("op_1101_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1108 = const()[name = tensor<string, []>("op_1108"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1109_cast = reshape(shape = var_1108, x = tensor_71_cast)[name = tensor<string, []>("op_1109_cast")];
            tensor<int32, [4]> var_1110_perm_0 = const()[name = tensor<string, []>("op_1110_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1112 = const()[name = tensor<string, []>("op_1112"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_59 = transpose(perm = var_1110_perm_0, x = var_1109_cast)[name = tensor<string, []>("transpose_59")];
            tensor<fp16, [16, 77, 64]> query_states_23_cast = reshape(shape = var_1112, x = transpose_59)[name = tensor<string, []>("query_states_23_cast")];
            tensor<int32, [3]> var_1114 = const()[name = tensor<string, []>("op_1114"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_58 = transpose(perm = var_1094_perm_0, x = var_1093_cast)[name = tensor<string, []>("transpose_58")];
            tensor<fp16, [16, 77, 64]> key_states_47_cast = reshape(shape = var_1114, x = transpose_58)[name = tensor<string, []>("key_states_47_cast")];
            tensor<int32, [3]> var_1116 = const()[name = tensor<string, []>("op_1116"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_57 = transpose(perm = var_1101_perm_0, x = var_1100_cast)[name = tensor<string, []>("transpose_57")];
            tensor<fp16, [16, 77, 64]> value_states_47_cast = reshape(shape = var_1116, x = transpose_57)[name = tensor<string, []>("value_states_47_cast")];
            tensor<int32, [3]> var_1119_perm_0 = const()[name = tensor<string, []>("op_1119_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_67_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_67_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_67_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_67_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_56 = transpose(perm = var_1119_perm_0, x = key_states_47_cast)[name = tensor<string, []>("transpose_56")];
            tensor<fp16, [16, 77, 77]> attn_weights_67_cast = matmul(transpose_x = attn_weights_67_transpose_x_0, transpose_y = attn_weights_67_transpose_y_0, x = query_states_23_cast, y = transpose_56)[name = tensor<string, []>("attn_weights_67_cast")];
            tensor<int32, [4]> var_1121 = const()[name = tensor<string, []>("op_1121"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1122_cast = reshape(shape = var_1121, x = attn_weights_67_cast)[name = tensor<string, []>("op_1122_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_69_cast = add(x = var_1122_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_69_cast")];
            tensor<int32, [3]> var_1127 = const()[name = tensor<string, []>("op_1127"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_181_cast = reshape(shape = var_1127, x = attn_weights_69_cast)[name = tensor<string, []>("input_181_cast")];
            tensor<fp16, [16, 77, 77]> input_183_cast = softmax(axis = var_5, x = input_181_cast)[name = tensor<string, []>("input_183_cast")];
            tensor<bool, []> attn_output_67_transpose_x_0 = const()[name = tensor<string, []>("attn_output_67_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_67_transpose_y_0 = const()[name = tensor<string, []>("attn_output_67_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_67_cast = matmul(transpose_x = attn_output_67_transpose_x_0, transpose_y = attn_output_67_transpose_y_0, x = input_183_cast, y = value_states_47_cast)[name = tensor<string, []>("attn_output_67_cast")];
            tensor<int32, [4]> var_1132 = const()[name = tensor<string, []>("op_1132"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_69_cast = reshape(shape = var_1132, x = attn_output_67_cast)[name = tensor<string, []>("attn_output_69_cast")];
            tensor<int32, [4]> attn_output_71_perm_0 = const()[name = tensor<string, []>("attn_output_71_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1135 = const()[name = tensor<string, []>("op_1135"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_55 = transpose(perm = attn_output_71_perm_0, x = attn_output_69_cast)[name = tensor<string, []>("transpose_55")];
            tensor<fp16, [1, 77, 1024]> input_185_cast = reshape(shape = var_1135, x = transpose_55)[name = tensor<string, []>("input_185_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136834560))), lut = tensor<fp16, [4]>([-0x1.1d4p-6, -0x1.52cp-8, 0x1.58p-8, 0x1.1ep-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137096768)))];
            tensor<fp16, [1, 77, 1024]> linear_69_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16_palettized, x = input_185_cast)[name = tensor<string, []>("linear_69_cast")];
            tensor<fp16, [1, 77, 1024]> input_187_cast = add(x = input_179_cast, y = linear_69_cast)[name = tensor<string, []>("input_187_cast")];
            tensor<int32, [1]> input_189_axes_0 = const()[name = tensor<string, []>("input_189_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137098880)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137100992)))];
            tensor<fp16, [1, 77, 1024]> input_189_cast = layer_norm(axes = input_189_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16, x = input_187_cast)[name = tensor<string, []>("input_189_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137103104))), lut = tensor<fp16, [4]>([-0x1.428p-6, -0x1.854p-8, 0x1.7a4p-8, 0x1.3f8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138151744))), lut = tensor<fp16, [4]>([-0x1.f2cp-2, -0x1.754p-2, -0x1.b7p-3, 0x1.e3p-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_70_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16_palettized, x = input_189_cast)[name = tensor<string, []>("linear_70_cast")];
            tensor<string, []> input_193_mode_0 = const()[name = tensor<string, []>("input_193_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_193_cast = gelu(mode = input_193_mode_0, x = linear_70_cast)[name = tensor<string, []>("input_193_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(138152832))), lut = tensor<fp16, [4]>([-0x1.0c8p-6, -0x1.418p-8, 0x1.3f4p-8, 0x1.0b8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139201472)))];
            tensor<fp16, [1, 77, 1024]> linear_71_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16_palettized, x = input_193_cast)[name = tensor<string, []>("linear_71_cast")];
            tensor<fp16, [1, 77, 1024]> input_195_cast = add(x = input_187_cast, y = linear_71_cast)[name = tensor<string, []>("input_195_cast")];
            tensor<int32, [1]> hidden_states_73_axes_0 = const()[name = tensor<string, []>("hidden_states_73_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139203584)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139205696)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_73_cast = layer_norm(axes = hidden_states_73_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16, x = input_195_cast)[name = tensor<string, []>("hidden_states_73_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139207808))), lut = tensor<fp16, [4]>([-0x1.594p-6, -0x1.998p-8, 0x1.a08p-8, 0x1.5acp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139470016)))];
            tensor<fp16, [1, 77, 1024]> linear_72_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("linear_72_cast")];
            tensor<fp16, []> var_1174_to_fp16 = const()[name = tensor<string, []>("op_1174_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_77_cast = mul(x = linear_72_cast, y = var_1174_to_fp16)[name = tensor<string, []>("tensor_77_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139472128))), lut = tensor<fp16, [4]>([-0x1.598p-6, -0x1.ap-8, 0x1.964p-8, 0x1.56cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139734336)))];
            tensor<fp16, [1, 77, 1024]> linear_73_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("linear_73_cast")];
            tensor<int32, [4]> var_1179 = const()[name = tensor<string, []>("op_1179"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1180_cast = reshape(shape = var_1179, x = linear_73_cast)[name = tensor<string, []>("op_1180_cast")];
            tensor<int32, [4]> var_1181_perm_0 = const()[name = tensor<string, []>("op_1181_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139736448))), lut = tensor<fp16, [4]>([-0x1.264p-6, -0x1.5fcp-8, 0x1.5d8p-8, 0x1.258p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(139998656)))];
            tensor<fp16, [1, 77, 1024]> linear_74_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_73_cast)[name = tensor<string, []>("linear_74_cast")];
            tensor<int32, [4]> var_1186 = const()[name = tensor<string, []>("op_1186"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1187_cast = reshape(shape = var_1186, x = linear_74_cast)[name = tensor<string, []>("op_1187_cast")];
            tensor<int32, [4]> var_1188_perm_0 = const()[name = tensor<string, []>("op_1188_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1195 = const()[name = tensor<string, []>("op_1195"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1196_cast = reshape(shape = var_1195, x = tensor_77_cast)[name = tensor<string, []>("op_1196_cast")];
            tensor<int32, [4]> var_1197_perm_0 = const()[name = tensor<string, []>("op_1197_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1199 = const()[name = tensor<string, []>("op_1199"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_54 = transpose(perm = var_1197_perm_0, x = var_1196_cast)[name = tensor<string, []>("transpose_54")];
            tensor<fp16, [16, 77, 64]> query_states_25_cast = reshape(shape = var_1199, x = transpose_54)[name = tensor<string, []>("query_states_25_cast")];
            tensor<int32, [3]> var_1201 = const()[name = tensor<string, []>("op_1201"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_53 = transpose(perm = var_1181_perm_0, x = var_1180_cast)[name = tensor<string, []>("transpose_53")];
            tensor<fp16, [16, 77, 64]> key_states_51_cast = reshape(shape = var_1201, x = transpose_53)[name = tensor<string, []>("key_states_51_cast")];
            tensor<int32, [3]> var_1203 = const()[name = tensor<string, []>("op_1203"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_52 = transpose(perm = var_1188_perm_0, x = var_1187_cast)[name = tensor<string, []>("transpose_52")];
            tensor<fp16, [16, 77, 64]> value_states_51_cast = reshape(shape = var_1203, x = transpose_52)[name = tensor<string, []>("value_states_51_cast")];
            tensor<int32, [3]> var_1206_perm_0 = const()[name = tensor<string, []>("op_1206_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_73_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_73_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_73_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_73_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_51 = transpose(perm = var_1206_perm_0, x = key_states_51_cast)[name = tensor<string, []>("transpose_51")];
            tensor<fp16, [16, 77, 77]> attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = query_states_25_cast, y = transpose_51)[name = tensor<string, []>("attn_weights_73_cast")];
            tensor<int32, [4]> var_1208 = const()[name = tensor<string, []>("op_1208"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1209_cast = reshape(shape = var_1208, x = attn_weights_73_cast)[name = tensor<string, []>("op_1209_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_75_cast = add(x = var_1209_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_75_cast")];
            tensor<int32, [3]> var_1214 = const()[name = tensor<string, []>("op_1214"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_197_cast = reshape(shape = var_1214, x = attn_weights_75_cast)[name = tensor<string, []>("input_197_cast")];
            tensor<fp16, [16, 77, 77]> input_199_cast = softmax(axis = var_5, x = input_197_cast)[name = tensor<string, []>("input_199_cast")];
            tensor<bool, []> attn_output_73_transpose_x_0 = const()[name = tensor<string, []>("attn_output_73_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_73_transpose_y_0 = const()[name = tensor<string, []>("attn_output_73_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_73_cast = matmul(transpose_x = attn_output_73_transpose_x_0, transpose_y = attn_output_73_transpose_y_0, x = input_199_cast, y = value_states_51_cast)[name = tensor<string, []>("attn_output_73_cast")];
            tensor<int32, [4]> var_1219 = const()[name = tensor<string, []>("op_1219"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_75_cast = reshape(shape = var_1219, x = attn_output_73_cast)[name = tensor<string, []>("attn_output_75_cast")];
            tensor<int32, [4]> attn_output_77_perm_0 = const()[name = tensor<string, []>("attn_output_77_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1222 = const()[name = tensor<string, []>("op_1222"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_50 = transpose(perm = attn_output_77_perm_0, x = attn_output_75_cast)[name = tensor<string, []>("transpose_50")];
            tensor<fp16, [1, 77, 1024]> input_201_cast = reshape(shape = var_1222, x = transpose_50)[name = tensor<string, []>("input_201_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140000768))), lut = tensor<fp16, [4]>([-0x1.24cp-6, -0x1.5e4p-8, 0x1.5ccp-8, 0x1.24p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140262976)))];
            tensor<fp16, [1, 77, 1024]> linear_75_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16_palettized, x = input_201_cast)[name = tensor<string, []>("linear_75_cast")];
            tensor<fp16, [1, 77, 1024]> input_203_cast = add(x = input_195_cast, y = linear_75_cast)[name = tensor<string, []>("input_203_cast")];
            tensor<int32, [1]> input_205_axes_0 = const()[name = tensor<string, []>("input_205_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140265088)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140267200)))];
            tensor<fp16, [1, 77, 1024]> input_205_cast = layer_norm(axes = input_205_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16, x = input_203_cast)[name = tensor<string, []>("input_205_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140269312))), lut = tensor<fp16, [4]>([-0x1.3fp-6, -0x1.7ccp-8, 0x1.7ep-8, 0x1.3f8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141317952))), lut = tensor<fp16, [4]>([-0x1.068p-1, -0x1.2d4p-2, -0x1.9acp-2, 0x1.104p-3]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_76_cast = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16_palettized, x = input_205_cast)[name = tensor<string, []>("linear_76_cast")];
            tensor<string, []> input_209_mode_0 = const()[name = tensor<string, []>("input_209_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_209_cast = gelu(mode = input_209_mode_0, x = linear_76_cast)[name = tensor<string, []>("input_209_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141319040))), lut = tensor<fp16, [4]>([-0x1.0c4p-6, -0x1.40cp-8, 0x1.414p-8, 0x1.0c8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142367680)))];
            tensor<fp16, [1, 77, 1024]> linear_77_cast = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16_palettized, x = input_209_cast)[name = tensor<string, []>("linear_77_cast")];
            tensor<fp16, [1, 77, 1024]> input_211_cast = add(x = input_203_cast, y = linear_77_cast)[name = tensor<string, []>("input_211_cast")];
            tensor<int32, [1]> hidden_states_79_axes_0 = const()[name = tensor<string, []>("hidden_states_79_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142369792)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142371904)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_79_cast = layer_norm(axes = hidden_states_79_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16, x = input_211_cast)[name = tensor<string, []>("hidden_states_79_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142374016))), lut = tensor<fp16, [4]>([-0x1.5bcp-6, -0x1.a3p-8, 0x1.994p-8, 0x1.59cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142636224)))];
            tensor<fp16, [1, 77, 1024]> linear_78_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("linear_78_cast")];
            tensor<fp16, []> var_1261_to_fp16 = const()[name = tensor<string, []>("op_1261_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_83_cast = mul(x = linear_78_cast, y = var_1261_to_fp16)[name = tensor<string, []>("tensor_83_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142638336))), lut = tensor<fp16, [4]>([-0x1.594p-6, -0x1.9c4p-8, 0x1.9dp-8, 0x1.594p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142900544)))];
            tensor<fp16, [1, 77, 1024]> linear_79_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("linear_79_cast")];
            tensor<int32, [4]> var_1266 = const()[name = tensor<string, []>("op_1266"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1267_cast = reshape(shape = var_1266, x = linear_79_cast)[name = tensor<string, []>("op_1267_cast")];
            tensor<int32, [4]> var_1268_perm_0 = const()[name = tensor<string, []>("op_1268_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(142902656))), lut = tensor<fp16, [4]>([-0x1.244p-6, -0x1.5c8p-8, 0x1.5b8p-8, 0x1.24p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143164864)))];
            tensor<fp16, [1, 77, 1024]> linear_80_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_79_cast)[name = tensor<string, []>("linear_80_cast")];
            tensor<int32, [4]> var_1273 = const()[name = tensor<string, []>("op_1273"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1274_cast = reshape(shape = var_1273, x = linear_80_cast)[name = tensor<string, []>("op_1274_cast")];
            tensor<int32, [4]> var_1275_perm_0 = const()[name = tensor<string, []>("op_1275_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1282 = const()[name = tensor<string, []>("op_1282"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1283_cast = reshape(shape = var_1282, x = tensor_83_cast)[name = tensor<string, []>("op_1283_cast")];
            tensor<int32, [4]> var_1284_perm_0 = const()[name = tensor<string, []>("op_1284_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1286 = const()[name = tensor<string, []>("op_1286"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_49 = transpose(perm = var_1284_perm_0, x = var_1283_cast)[name = tensor<string, []>("transpose_49")];
            tensor<fp16, [16, 77, 64]> query_states_27_cast = reshape(shape = var_1286, x = transpose_49)[name = tensor<string, []>("query_states_27_cast")];
            tensor<int32, [3]> var_1288 = const()[name = tensor<string, []>("op_1288"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_48 = transpose(perm = var_1268_perm_0, x = var_1267_cast)[name = tensor<string, []>("transpose_48")];
            tensor<fp16, [16, 77, 64]> key_states_55_cast = reshape(shape = var_1288, x = transpose_48)[name = tensor<string, []>("key_states_55_cast")];
            tensor<int32, [3]> var_1290 = const()[name = tensor<string, []>("op_1290"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_47 = transpose(perm = var_1275_perm_0, x = var_1274_cast)[name = tensor<string, []>("transpose_47")];
            tensor<fp16, [16, 77, 64]> value_states_55_cast = reshape(shape = var_1290, x = transpose_47)[name = tensor<string, []>("value_states_55_cast")];
            tensor<int32, [3]> var_1293_perm_0 = const()[name = tensor<string, []>("op_1293_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_79_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_79_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_79_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_79_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_46 = transpose(perm = var_1293_perm_0, x = key_states_55_cast)[name = tensor<string, []>("transpose_46")];
            tensor<fp16, [16, 77, 77]> attn_weights_79_cast = matmul(transpose_x = attn_weights_79_transpose_x_0, transpose_y = attn_weights_79_transpose_y_0, x = query_states_27_cast, y = transpose_46)[name = tensor<string, []>("attn_weights_79_cast")];
            tensor<int32, [4]> var_1295 = const()[name = tensor<string, []>("op_1295"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1296_cast = reshape(shape = var_1295, x = attn_weights_79_cast)[name = tensor<string, []>("op_1296_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_81_cast = add(x = var_1296_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_81_cast")];
            tensor<int32, [3]> var_1301 = const()[name = tensor<string, []>("op_1301"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_213_cast = reshape(shape = var_1301, x = attn_weights_81_cast)[name = tensor<string, []>("input_213_cast")];
            tensor<fp16, [16, 77, 77]> input_215_cast = softmax(axis = var_5, x = input_213_cast)[name = tensor<string, []>("input_215_cast")];
            tensor<bool, []> attn_output_79_transpose_x_0 = const()[name = tensor<string, []>("attn_output_79_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_79_transpose_y_0 = const()[name = tensor<string, []>("attn_output_79_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_79_cast = matmul(transpose_x = attn_output_79_transpose_x_0, transpose_y = attn_output_79_transpose_y_0, x = input_215_cast, y = value_states_55_cast)[name = tensor<string, []>("attn_output_79_cast")];
            tensor<int32, [4]> var_1306 = const()[name = tensor<string, []>("op_1306"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_81_cast = reshape(shape = var_1306, x = attn_output_79_cast)[name = tensor<string, []>("attn_output_81_cast")];
            tensor<int32, [4]> attn_output_83_perm_0 = const()[name = tensor<string, []>("attn_output_83_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1309 = const()[name = tensor<string, []>("op_1309"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_45 = transpose(perm = attn_output_83_perm_0, x = attn_output_81_cast)[name = tensor<string, []>("transpose_45")];
            tensor<fp16, [1, 77, 1024]> input_217_cast = reshape(shape = var_1309, x = transpose_45)[name = tensor<string, []>("input_217_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143166976))), lut = tensor<fp16, [4]>([-0x1.24p-6, -0x1.5fp-8, 0x1.5a4p-8, 0x1.224p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143429184)))];
            tensor<fp16, [1, 77, 1024]> linear_81_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16_palettized, x = input_217_cast)[name = tensor<string, []>("linear_81_cast")];
            tensor<fp16, [1, 77, 1024]> input_219_cast = add(x = input_211_cast, y = linear_81_cast)[name = tensor<string, []>("input_219_cast")];
            tensor<int32, [1]> input_221_axes_0 = const()[name = tensor<string, []>("input_221_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143431296)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143433408)))];
            tensor<fp16, [1, 77, 1024]> input_221_cast = layer_norm(axes = input_221_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16, x = input_219_cast)[name = tensor<string, []>("input_221_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143435520))), lut = tensor<fp16, [4]>([-0x1.3cp-6, -0x1.74p-8, 0x1.868p-8, 0x1.414p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144484160))), lut = tensor<fp16, [4]>([-0x1.3ecp-2, -0x1.12p-1, 0x1.4ccp-3, -0x1.a9p-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_82_cast = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16_palettized, x = input_221_cast)[name = tensor<string, []>("linear_82_cast")];
            tensor<string, []> input_225_mode_0 = const()[name = tensor<string, []>("input_225_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_225_cast = gelu(mode = input_225_mode_0, x = linear_82_cast)[name = tensor<string, []>("input_225_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144485248))), lut = tensor<fp16, [4]>([-0x1.0d8p-6, -0x1.41cp-8, 0x1.438p-8, 0x1.0ep-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145533888)))];
            tensor<fp16, [1, 77, 1024]> linear_83_cast = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16_palettized, x = input_225_cast)[name = tensor<string, []>("linear_83_cast")];
            tensor<fp16, [1, 77, 1024]> input_227_cast = add(x = input_219_cast, y = linear_83_cast)[name = tensor<string, []>("input_227_cast")];
            tensor<int32, [1]> hidden_states_85_axes_0 = const()[name = tensor<string, []>("hidden_states_85_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145536000)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145538112)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_85_cast = layer_norm(axes = hidden_states_85_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16, x = input_227_cast)[name = tensor<string, []>("hidden_states_85_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145540224))), lut = tensor<fp16, [4]>([-0x1.5bcp-6, -0x1.9fp-8, 0x1.a28p-8, 0x1.5dp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145802432)))];
            tensor<fp16, [1, 77, 1024]> linear_84_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("linear_84_cast")];
            tensor<fp16, []> var_1348_to_fp16 = const()[name = tensor<string, []>("op_1348_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_89_cast = mul(x = linear_84_cast, y = var_1348_to_fp16)[name = tensor<string, []>("tensor_89_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145804544))), lut = tensor<fp16, [4]>([-0x1.598p-6, -0x1.9ccp-8, 0x1.9d8p-8, 0x1.59cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146066752)))];
            tensor<fp16, [1, 77, 1024]> linear_85_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("linear_85_cast")];
            tensor<int32, [4]> var_1353 = const()[name = tensor<string, []>("op_1353"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1354_cast = reshape(shape = var_1353, x = linear_85_cast)[name = tensor<string, []>("op_1354_cast")];
            tensor<int32, [4]> var_1355_perm_0 = const()[name = tensor<string, []>("op_1355_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146068864))), lut = tensor<fp16, [4]>([-0x1.2c4p-6, -0x1.644p-8, 0x1.67cp-8, 0x1.2ccp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146331072)))];
            tensor<fp16, [1, 77, 1024]> linear_86_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_85_cast)[name = tensor<string, []>("linear_86_cast")];
            tensor<int32, [4]> var_1360 = const()[name = tensor<string, []>("op_1360"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1361_cast = reshape(shape = var_1360, x = linear_86_cast)[name = tensor<string, []>("op_1361_cast")];
            tensor<int32, [4]> var_1362_perm_0 = const()[name = tensor<string, []>("op_1362_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1369 = const()[name = tensor<string, []>("op_1369"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1370_cast = reshape(shape = var_1369, x = tensor_89_cast)[name = tensor<string, []>("op_1370_cast")];
            tensor<int32, [4]> var_1371_perm_0 = const()[name = tensor<string, []>("op_1371_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1373 = const()[name = tensor<string, []>("op_1373"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_44 = transpose(perm = var_1371_perm_0, x = var_1370_cast)[name = tensor<string, []>("transpose_44")];
            tensor<fp16, [16, 77, 64]> query_states_29_cast = reshape(shape = var_1373, x = transpose_44)[name = tensor<string, []>("query_states_29_cast")];
            tensor<int32, [3]> var_1375 = const()[name = tensor<string, []>("op_1375"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_43 = transpose(perm = var_1355_perm_0, x = var_1354_cast)[name = tensor<string, []>("transpose_43")];
            tensor<fp16, [16, 77, 64]> key_states_59_cast = reshape(shape = var_1375, x = transpose_43)[name = tensor<string, []>("key_states_59_cast")];
            tensor<int32, [3]> var_1377 = const()[name = tensor<string, []>("op_1377"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_42 = transpose(perm = var_1362_perm_0, x = var_1361_cast)[name = tensor<string, []>("transpose_42")];
            tensor<fp16, [16, 77, 64]> value_states_59_cast = reshape(shape = var_1377, x = transpose_42)[name = tensor<string, []>("value_states_59_cast")];
            tensor<int32, [3]> var_1380_perm_0 = const()[name = tensor<string, []>("op_1380_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_85_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_85_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_85_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_85_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_41 = transpose(perm = var_1380_perm_0, x = key_states_59_cast)[name = tensor<string, []>("transpose_41")];
            tensor<fp16, [16, 77, 77]> attn_weights_85_cast = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = query_states_29_cast, y = transpose_41)[name = tensor<string, []>("attn_weights_85_cast")];
            tensor<int32, [4]> var_1382 = const()[name = tensor<string, []>("op_1382"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1383_cast = reshape(shape = var_1382, x = attn_weights_85_cast)[name = tensor<string, []>("op_1383_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_87_cast = add(x = var_1383_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_87_cast")];
            tensor<int32, [3]> var_1388 = const()[name = tensor<string, []>("op_1388"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_229_cast = reshape(shape = var_1388, x = attn_weights_87_cast)[name = tensor<string, []>("input_229_cast")];
            tensor<fp16, [16, 77, 77]> input_231_cast = softmax(axis = var_5, x = input_229_cast)[name = tensor<string, []>("input_231_cast")];
            tensor<bool, []> attn_output_85_transpose_x_0 = const()[name = tensor<string, []>("attn_output_85_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_85_transpose_y_0 = const()[name = tensor<string, []>("attn_output_85_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_85_cast = matmul(transpose_x = attn_output_85_transpose_x_0, transpose_y = attn_output_85_transpose_y_0, x = input_231_cast, y = value_states_59_cast)[name = tensor<string, []>("attn_output_85_cast")];
            tensor<int32, [4]> var_1393 = const()[name = tensor<string, []>("op_1393"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_87_cast = reshape(shape = var_1393, x = attn_output_85_cast)[name = tensor<string, []>("attn_output_87_cast")];
            tensor<int32, [4]> attn_output_89_perm_0 = const()[name = tensor<string, []>("attn_output_89_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1396 = const()[name = tensor<string, []>("op_1396"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_40 = transpose(perm = attn_output_89_perm_0, x = attn_output_87_cast)[name = tensor<string, []>("transpose_40")];
            tensor<fp16, [1, 77, 1024]> input_233_cast = reshape(shape = var_1396, x = transpose_40)[name = tensor<string, []>("input_233_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146333184))), lut = tensor<fp16, [4]>([-0x1.2bp-6, -0x1.65p-8, 0x1.67p-8, 0x1.2b8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146595392)))];
            tensor<fp16, [1, 77, 1024]> linear_87_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16_palettized, x = input_233_cast)[name = tensor<string, []>("linear_87_cast")];
            tensor<fp16, [1, 77, 1024]> input_235_cast = add(x = input_227_cast, y = linear_87_cast)[name = tensor<string, []>("input_235_cast")];
            tensor<int32, [1]> input_237_axes_0 = const()[name = tensor<string, []>("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146597504)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146599616)))];
            tensor<fp16, [1, 77, 1024]> input_237_cast = layer_norm(axes = input_237_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16, x = input_235_cast)[name = tensor<string, []>("input_237_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146601728))), lut = tensor<fp16, [4]>([-0x1.3bcp-6, -0x1.768p-8, 0x1.82p-8, 0x1.3f8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147650368))), lut = tensor<fp16, [4]>([-0x1.938p-2, -0x1.054p-1, -0x1.2c8p-2, 0x1.098p-3]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_88_cast = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16_palettized, x = input_237_cast)[name = tensor<string, []>("linear_88_cast")];
            tensor<string, []> input_241_mode_0 = const()[name = tensor<string, []>("input_241_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_241_cast = gelu(mode = input_241_mode_0, x = linear_88_cast)[name = tensor<string, []>("input_241_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147651456))), lut = tensor<fp16, [4]>([-0x1.0e4p-6, -0x1.434p-8, 0x1.434p-8, 0x1.0e4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148700096)))];
            tensor<fp16, [1, 77, 1024]> linear_89_cast = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16_palettized, x = input_241_cast)[name = tensor<string, []>("linear_89_cast")];
            tensor<fp16, [1, 77, 1024]> input_243_cast = add(x = input_235_cast, y = linear_89_cast)[name = tensor<string, []>("input_243_cast")];
            tensor<int32, [1]> hidden_states_91_axes_0 = const()[name = tensor<string, []>("hidden_states_91_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148702208)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148704320)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_91_cast = layer_norm(axes = hidden_states_91_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16, x = input_243_cast)[name = tensor<string, []>("hidden_states_91_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148706432))), lut = tensor<fp16, [4]>([-0x1.5b4p-6, -0x1.9e4p-8, 0x1.ap-8, 0x1.5cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148968640)))];
            tensor<fp16, [1, 77, 1024]> linear_90_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("linear_90_cast")];
            tensor<fp16, []> var_1435_to_fp16 = const()[name = tensor<string, []>("op_1435_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_95_cast = mul(x = linear_90_cast, y = var_1435_to_fp16)[name = tensor<string, []>("tensor_95_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148970752))), lut = tensor<fp16, [4]>([-0x1.5a4p-6, -0x1.a18p-8, 0x1.97p-8, 0x1.58p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149232960)))];
            tensor<fp16, [1, 77, 1024]> linear_91_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("linear_91_cast")];
            tensor<int32, [4]> var_1440 = const()[name = tensor<string, []>("op_1440"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1441_cast = reshape(shape = var_1440, x = linear_91_cast)[name = tensor<string, []>("op_1441_cast")];
            tensor<int32, [4]> var_1442_perm_0 = const()[name = tensor<string, []>("op_1442_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149235072))), lut = tensor<fp16, [4]>([-0x1.324p-6, -0x1.6cp-8, 0x1.6d8p-8, 0x1.33p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149497280)))];
            tensor<fp16, [1, 77, 1024]> linear_92_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_91_cast)[name = tensor<string, []>("linear_92_cast")];
            tensor<int32, [4]> var_1447 = const()[name = tensor<string, []>("op_1447"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1448_cast = reshape(shape = var_1447, x = linear_92_cast)[name = tensor<string, []>("op_1448_cast")];
            tensor<int32, [4]> var_1449_perm_0 = const()[name = tensor<string, []>("op_1449_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1456 = const()[name = tensor<string, []>("op_1456"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1457_cast = reshape(shape = var_1456, x = tensor_95_cast)[name = tensor<string, []>("op_1457_cast")];
            tensor<int32, [4]> var_1458_perm_0 = const()[name = tensor<string, []>("op_1458_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1460 = const()[name = tensor<string, []>("op_1460"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_39 = transpose(perm = var_1458_perm_0, x = var_1457_cast)[name = tensor<string, []>("transpose_39")];
            tensor<fp16, [16, 77, 64]> query_states_31_cast = reshape(shape = var_1460, x = transpose_39)[name = tensor<string, []>("query_states_31_cast")];
            tensor<int32, [3]> var_1462 = const()[name = tensor<string, []>("op_1462"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_38 = transpose(perm = var_1442_perm_0, x = var_1441_cast)[name = tensor<string, []>("transpose_38")];
            tensor<fp16, [16, 77, 64]> key_states_63_cast = reshape(shape = var_1462, x = transpose_38)[name = tensor<string, []>("key_states_63_cast")];
            tensor<int32, [3]> var_1464 = const()[name = tensor<string, []>("op_1464"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_37 = transpose(perm = var_1449_perm_0, x = var_1448_cast)[name = tensor<string, []>("transpose_37")];
            tensor<fp16, [16, 77, 64]> value_states_63_cast = reshape(shape = var_1464, x = transpose_37)[name = tensor<string, []>("value_states_63_cast")];
            tensor<int32, [3]> var_1467_perm_0 = const()[name = tensor<string, []>("op_1467_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_91_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_91_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_91_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_91_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_36 = transpose(perm = var_1467_perm_0, x = key_states_63_cast)[name = tensor<string, []>("transpose_36")];
            tensor<fp16, [16, 77, 77]> attn_weights_91_cast = matmul(transpose_x = attn_weights_91_transpose_x_0, transpose_y = attn_weights_91_transpose_y_0, x = query_states_31_cast, y = transpose_36)[name = tensor<string, []>("attn_weights_91_cast")];
            tensor<int32, [4]> var_1469 = const()[name = tensor<string, []>("op_1469"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1470_cast = reshape(shape = var_1469, x = attn_weights_91_cast)[name = tensor<string, []>("op_1470_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_93_cast = add(x = var_1470_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_93_cast")];
            tensor<int32, [3]> var_1475 = const()[name = tensor<string, []>("op_1475"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_245_cast = reshape(shape = var_1475, x = attn_weights_93_cast)[name = tensor<string, []>("input_245_cast")];
            tensor<fp16, [16, 77, 77]> input_247_cast = softmax(axis = var_5, x = input_245_cast)[name = tensor<string, []>("input_247_cast")];
            tensor<bool, []> attn_output_91_transpose_x_0 = const()[name = tensor<string, []>("attn_output_91_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_91_transpose_y_0 = const()[name = tensor<string, []>("attn_output_91_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_91_cast = matmul(transpose_x = attn_output_91_transpose_x_0, transpose_y = attn_output_91_transpose_y_0, x = input_247_cast, y = value_states_63_cast)[name = tensor<string, []>("attn_output_91_cast")];
            tensor<int32, [4]> var_1480 = const()[name = tensor<string, []>("op_1480"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_93_cast = reshape(shape = var_1480, x = attn_output_91_cast)[name = tensor<string, []>("attn_output_93_cast")];
            tensor<int32, [4]> attn_output_95_perm_0 = const()[name = tensor<string, []>("attn_output_95_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1483 = const()[name = tensor<string, []>("op_1483"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_35 = transpose(perm = attn_output_95_perm_0, x = attn_output_93_cast)[name = tensor<string, []>("transpose_35")];
            tensor<fp16, [1, 77, 1024]> input_249_cast = reshape(shape = var_1483, x = transpose_35)[name = tensor<string, []>("input_249_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149499392))), lut = tensor<fp16, [4]>([-0x1.31p-6, -0x1.69cp-8, 0x1.714p-8, 0x1.328p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149761600)))];
            tensor<fp16, [1, 77, 1024]> linear_93_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16_palettized, x = input_249_cast)[name = tensor<string, []>("linear_93_cast")];
            tensor<fp16, [1, 77, 1024]> input_251_cast = add(x = input_243_cast, y = linear_93_cast)[name = tensor<string, []>("input_251_cast")];
            tensor<int32, [1]> input_253_axes_0 = const()[name = tensor<string, []>("input_253_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149763712)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149765824)))];
            tensor<fp16, [1, 77, 1024]> input_253_cast = layer_norm(axes = input_253_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16, x = input_251_cast)[name = tensor<string, []>("input_253_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149767936))), lut = tensor<fp16, [4]>([-0x1.3b4p-6, -0x1.758p-8, 0x1.814p-8, 0x1.3e8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150816576))), lut = tensor<fp16, [4]>([-0x1.51p-2, -0x1.158p-1, 0x1.5cp-3, -0x1.b94p-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_94_cast = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16_palettized, x = input_253_cast)[name = tensor<string, []>("linear_94_cast")];
            tensor<string, []> input_257_mode_0 = const()[name = tensor<string, []>("input_257_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_257_cast = gelu(mode = input_257_mode_0, x = linear_94_cast)[name = tensor<string, []>("input_257_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150817664))), lut = tensor<fp16, [4]>([-0x1.1p-6, -0x1.444p-8, 0x1.46p-8, 0x1.104p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151866304)))];
            tensor<fp16, [1, 77, 1024]> linear_95_cast = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16_palettized, x = input_257_cast)[name = tensor<string, []>("linear_95_cast")];
            tensor<fp16, [1, 77, 1024]> input_259_cast = add(x = input_251_cast, y = linear_95_cast)[name = tensor<string, []>("input_259_cast")];
            tensor<int32, [1]> hidden_states_97_axes_0 = const()[name = tensor<string, []>("hidden_states_97_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151868416)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151870528)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_97_cast = layer_norm(axes = hidden_states_97_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16, x = input_259_cast)[name = tensor<string, []>("hidden_states_97_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(151872640))), lut = tensor<fp16, [4]>([-0x1.5b4p-6, -0x1.9d4p-8, 0x1.a24p-8, 0x1.5c4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152134848)))];
            tensor<fp16, [1, 77, 1024]> linear_96_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("linear_96_cast")];
            tensor<fp16, []> var_1522_to_fp16 = const()[name = tensor<string, []>("op_1522_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_101_cast = mul(x = linear_96_cast, y = var_1522_to_fp16)[name = tensor<string, []>("tensor_101_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152136960))), lut = tensor<fp16, [4]>([-0x1.5bcp-6, -0x1.ap-8, 0x1.9fp-8, 0x1.5b8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152399168)))];
            tensor<fp16, [1, 77, 1024]> linear_97_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("linear_97_cast")];
            tensor<int32, [4]> var_1527 = const()[name = tensor<string, []>("op_1527"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1528_cast = reshape(shape = var_1527, x = linear_97_cast)[name = tensor<string, []>("op_1528_cast")];
            tensor<int32, [4]> var_1529_perm_0 = const()[name = tensor<string, []>("op_1529_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152401280))), lut = tensor<fp16, [4]>([-0x1.254p-6, -0x1.5dp-8, 0x1.5e8p-8, 0x1.258p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152663488)))];
            tensor<fp16, [1, 77, 1024]> linear_98_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_97_cast)[name = tensor<string, []>("linear_98_cast")];
            tensor<int32, [4]> var_1534 = const()[name = tensor<string, []>("op_1534"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1535_cast = reshape(shape = var_1534, x = linear_98_cast)[name = tensor<string, []>("op_1535_cast")];
            tensor<int32, [4]> var_1536_perm_0 = const()[name = tensor<string, []>("op_1536_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1543 = const()[name = tensor<string, []>("op_1543"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1544_cast = reshape(shape = var_1543, x = tensor_101_cast)[name = tensor<string, []>("op_1544_cast")];
            tensor<int32, [4]> var_1545_perm_0 = const()[name = tensor<string, []>("op_1545_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1547 = const()[name = tensor<string, []>("op_1547"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_34 = transpose(perm = var_1545_perm_0, x = var_1544_cast)[name = tensor<string, []>("transpose_34")];
            tensor<fp16, [16, 77, 64]> query_states_33_cast = reshape(shape = var_1547, x = transpose_34)[name = tensor<string, []>("query_states_33_cast")];
            tensor<int32, [3]> var_1549 = const()[name = tensor<string, []>("op_1549"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_33 = transpose(perm = var_1529_perm_0, x = var_1528_cast)[name = tensor<string, []>("transpose_33")];
            tensor<fp16, [16, 77, 64]> key_states_67_cast = reshape(shape = var_1549, x = transpose_33)[name = tensor<string, []>("key_states_67_cast")];
            tensor<int32, [3]> var_1551 = const()[name = tensor<string, []>("op_1551"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_32 = transpose(perm = var_1536_perm_0, x = var_1535_cast)[name = tensor<string, []>("transpose_32")];
            tensor<fp16, [16, 77, 64]> value_states_67_cast = reshape(shape = var_1551, x = transpose_32)[name = tensor<string, []>("value_states_67_cast")];
            tensor<int32, [3]> var_1554_perm_0 = const()[name = tensor<string, []>("op_1554_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_97_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_97_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_97_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_97_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_31 = transpose(perm = var_1554_perm_0, x = key_states_67_cast)[name = tensor<string, []>("transpose_31")];
            tensor<fp16, [16, 77, 77]> attn_weights_97_cast = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = query_states_33_cast, y = transpose_31)[name = tensor<string, []>("attn_weights_97_cast")];
            tensor<int32, [4]> var_1556 = const()[name = tensor<string, []>("op_1556"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1557_cast = reshape(shape = var_1556, x = attn_weights_97_cast)[name = tensor<string, []>("op_1557_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_99_cast = add(x = var_1557_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_99_cast")];
            tensor<int32, [3]> var_1562 = const()[name = tensor<string, []>("op_1562"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_261_cast = reshape(shape = var_1562, x = attn_weights_99_cast)[name = tensor<string, []>("input_261_cast")];
            tensor<fp16, [16, 77, 77]> input_263_cast = softmax(axis = var_5, x = input_261_cast)[name = tensor<string, []>("input_263_cast")];
            tensor<bool, []> attn_output_97_transpose_x_0 = const()[name = tensor<string, []>("attn_output_97_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_97_transpose_y_0 = const()[name = tensor<string, []>("attn_output_97_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_97_cast = matmul(transpose_x = attn_output_97_transpose_x_0, transpose_y = attn_output_97_transpose_y_0, x = input_263_cast, y = value_states_67_cast)[name = tensor<string, []>("attn_output_97_cast")];
            tensor<int32, [4]> var_1567 = const()[name = tensor<string, []>("op_1567"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_99_cast = reshape(shape = var_1567, x = attn_output_97_cast)[name = tensor<string, []>("attn_output_99_cast")];
            tensor<int32, [4]> attn_output_101_perm_0 = const()[name = tensor<string, []>("attn_output_101_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1570 = const()[name = tensor<string, []>("op_1570"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_30 = transpose(perm = attn_output_101_perm_0, x = attn_output_99_cast)[name = tensor<string, []>("transpose_30")];
            tensor<fp16, [1, 77, 1024]> input_265_cast = reshape(shape = var_1570, x = transpose_30)[name = tensor<string, []>("input_265_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152665600))), lut = tensor<fp16, [4]>([-0x1.228p-6, -0x1.5b8p-8, 0x1.5b8p-8, 0x1.23p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152927808)))];
            tensor<fp16, [1, 77, 1024]> linear_99_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16_palettized, x = input_265_cast)[name = tensor<string, []>("linear_99_cast")];
            tensor<fp16, [1, 77, 1024]> input_267_cast = add(x = input_259_cast, y = linear_99_cast)[name = tensor<string, []>("input_267_cast")];
            tensor<int32, [1]> input_269_axes_0 = const()[name = tensor<string, []>("input_269_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152929920)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152932032)))];
            tensor<fp16, [1, 77, 1024]> input_269_cast = layer_norm(axes = input_269_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16, x = input_267_cast)[name = tensor<string, []>("input_269_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152934144))), lut = tensor<fp16, [4]>([-0x1.398p-6, -0x1.724p-8, 0x1.82p-8, 0x1.3ep-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153982784))), lut = tensor<fp16, [4]>([-0x1.b14p-2, -0x1.55cp-2, -0x1.0e8p-1, 0x1.3fp-5]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_100_cast = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16_palettized, x = input_269_cast)[name = tensor<string, []>("linear_100_cast")];
            tensor<string, []> input_273_mode_0 = const()[name = tensor<string, []>("input_273_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_273_cast = gelu(mode = input_273_mode_0, x = linear_100_cast)[name = tensor<string, []>("input_273_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153983872))), lut = tensor<fp16, [4]>([-0x1.1p-6, -0x1.45p-8, 0x1.454p-8, 0x1.0fcp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155032512)))];
            tensor<fp16, [1, 77, 1024]> linear_101_cast = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16_palettized, x = input_273_cast)[name = tensor<string, []>("linear_101_cast")];
            tensor<fp16, [1, 77, 1024]> input_275_cast = add(x = input_267_cast, y = linear_101_cast)[name = tensor<string, []>("input_275_cast")];
            tensor<int32, [1]> hidden_states_103_axes_0 = const()[name = tensor<string, []>("hidden_states_103_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155034624)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155036736)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_103_cast = layer_norm(axes = hidden_states_103_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16, x = input_275_cast)[name = tensor<string, []>("hidden_states_103_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155038848))), lut = tensor<fp16, [4]>([-0x1.594p-6, -0x1.9a8p-8, 0x1.ap-8, 0x1.5a4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155301056)))];
            tensor<fp16, [1, 77, 1024]> linear_102_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("linear_102_cast")];
            tensor<fp16, []> var_1609_to_fp16 = const()[name = tensor<string, []>("op_1609_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_107_cast = mul(x = linear_102_cast, y = var_1609_to_fp16)[name = tensor<string, []>("tensor_107_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155303168))), lut = tensor<fp16, [4]>([-0x1.59p-6, -0x1.998p-8, 0x1.a28p-8, 0x1.5bp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155565376)))];
            tensor<fp16, [1, 77, 1024]> linear_103_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("linear_103_cast")];
            tensor<int32, [4]> var_1614 = const()[name = tensor<string, []>("op_1614"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1615_cast = reshape(shape = var_1614, x = linear_103_cast)[name = tensor<string, []>("op_1615_cast")];
            tensor<int32, [4]> var_1616_perm_0 = const()[name = tensor<string, []>("op_1616_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155567488))), lut = tensor<fp16, [4]>([-0x1.274p-6, -0x1.61p-8, 0x1.5f8p-8, 0x1.274p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155829696)))];
            tensor<fp16, [1, 77, 1024]> linear_104_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_103_cast)[name = tensor<string, []>("linear_104_cast")];
            tensor<int32, [4]> var_1621 = const()[name = tensor<string, []>("op_1621"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1622_cast = reshape(shape = var_1621, x = linear_104_cast)[name = tensor<string, []>("op_1622_cast")];
            tensor<int32, [4]> var_1623_perm_0 = const()[name = tensor<string, []>("op_1623_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1630 = const()[name = tensor<string, []>("op_1630"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1631_cast = reshape(shape = var_1630, x = tensor_107_cast)[name = tensor<string, []>("op_1631_cast")];
            tensor<int32, [4]> var_1632_perm_0 = const()[name = tensor<string, []>("op_1632_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1634 = const()[name = tensor<string, []>("op_1634"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_29 = transpose(perm = var_1632_perm_0, x = var_1631_cast)[name = tensor<string, []>("transpose_29")];
            tensor<fp16, [16, 77, 64]> query_states_35_cast = reshape(shape = var_1634, x = transpose_29)[name = tensor<string, []>("query_states_35_cast")];
            tensor<int32, [3]> var_1636 = const()[name = tensor<string, []>("op_1636"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_28 = transpose(perm = var_1616_perm_0, x = var_1615_cast)[name = tensor<string, []>("transpose_28")];
            tensor<fp16, [16, 77, 64]> key_states_71_cast = reshape(shape = var_1636, x = transpose_28)[name = tensor<string, []>("key_states_71_cast")];
            tensor<int32, [3]> var_1638 = const()[name = tensor<string, []>("op_1638"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_27 = transpose(perm = var_1623_perm_0, x = var_1622_cast)[name = tensor<string, []>("transpose_27")];
            tensor<fp16, [16, 77, 64]> value_states_71_cast = reshape(shape = var_1638, x = transpose_27)[name = tensor<string, []>("value_states_71_cast")];
            tensor<int32, [3]> var_1641_perm_0 = const()[name = tensor<string, []>("op_1641_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_103_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_103_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_103_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_103_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_26 = transpose(perm = var_1641_perm_0, x = key_states_71_cast)[name = tensor<string, []>("transpose_26")];
            tensor<fp16, [16, 77, 77]> attn_weights_103_cast = matmul(transpose_x = attn_weights_103_transpose_x_0, transpose_y = attn_weights_103_transpose_y_0, x = query_states_35_cast, y = transpose_26)[name = tensor<string, []>("attn_weights_103_cast")];
            tensor<int32, [4]> var_1643 = const()[name = tensor<string, []>("op_1643"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1644_cast = reshape(shape = var_1643, x = attn_weights_103_cast)[name = tensor<string, []>("op_1644_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_105_cast = add(x = var_1644_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_105_cast")];
            tensor<int32, [3]> var_1649 = const()[name = tensor<string, []>("op_1649"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_277_cast = reshape(shape = var_1649, x = attn_weights_105_cast)[name = tensor<string, []>("input_277_cast")];
            tensor<fp16, [16, 77, 77]> input_279_cast = softmax(axis = var_5, x = input_277_cast)[name = tensor<string, []>("input_279_cast")];
            tensor<bool, []> attn_output_103_transpose_x_0 = const()[name = tensor<string, []>("attn_output_103_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_103_transpose_y_0 = const()[name = tensor<string, []>("attn_output_103_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_103_cast = matmul(transpose_x = attn_output_103_transpose_x_0, transpose_y = attn_output_103_transpose_y_0, x = input_279_cast, y = value_states_71_cast)[name = tensor<string, []>("attn_output_103_cast")];
            tensor<int32, [4]> var_1654 = const()[name = tensor<string, []>("op_1654"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_105_cast = reshape(shape = var_1654, x = attn_output_103_cast)[name = tensor<string, []>("attn_output_105_cast")];
            tensor<int32, [4]> attn_output_107_perm_0 = const()[name = tensor<string, []>("attn_output_107_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1657 = const()[name = tensor<string, []>("op_1657"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_25 = transpose(perm = attn_output_107_perm_0, x = attn_output_105_cast)[name = tensor<string, []>("transpose_25")];
            tensor<fp16, [1, 77, 1024]> input_281_cast = reshape(shape = var_1657, x = transpose_25)[name = tensor<string, []>("input_281_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155831808))), lut = tensor<fp16, [4]>([-0x1.254p-6, -0x1.5fp-8, 0x1.5e4p-8, 0x1.25p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156094016)))];
            tensor<fp16, [1, 77, 1024]> linear_105_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16_palettized, x = input_281_cast)[name = tensor<string, []>("linear_105_cast")];
            tensor<fp16, [1, 77, 1024]> input_283_cast = add(x = input_275_cast, y = linear_105_cast)[name = tensor<string, []>("input_283_cast")];
            tensor<int32, [1]> input_285_axes_0 = const()[name = tensor<string, []>("input_285_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156096128)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156098240)))];
            tensor<fp16, [1, 77, 1024]> input_285_cast = layer_norm(axes = input_285_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16, x = input_283_cast)[name = tensor<string, []>("input_285_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156100352))), lut = tensor<fp16, [4]>([-0x1.37cp-6, -0x1.6dcp-8, 0x1.83p-8, 0x1.3d4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(157148992))), lut = tensor<fp16, [4]>([-0x1.988p-2, 0x1.634p-3, -0x1.03p-1, -0x1.37cp-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_106_cast = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16_palettized, x = input_285_cast)[name = tensor<string, []>("linear_106_cast")];
            tensor<string, []> input_289_mode_0 = const()[name = tensor<string, []>("input_289_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_289_cast = gelu(mode = input_289_mode_0, x = linear_106_cast)[name = tensor<string, []>("input_289_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(157150080))), lut = tensor<fp16, [4]>([-0x1.124p-6, -0x1.49cp-8, 0x1.454p-8, 0x1.114p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158198720)))];
            tensor<fp16, [1, 77, 1024]> linear_107_cast = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16_palettized, x = input_289_cast)[name = tensor<string, []>("linear_107_cast")];
            tensor<fp16, [1, 77, 1024]> input_291_cast = add(x = input_283_cast, y = linear_107_cast)[name = tensor<string, []>("input_291_cast")];
            tensor<int32, [1]> hidden_states_109_axes_0 = const()[name = tensor<string, []>("hidden_states_109_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158200832)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158202944)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_109_cast = layer_norm(axes = hidden_states_109_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16, x = input_291_cast)[name = tensor<string, []>("hidden_states_109_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158205056))), lut = tensor<fp16, [4]>([-0x1.58cp-6, -0x1.998p-8, 0x1.a08p-8, 0x1.598p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158467264)))];
            tensor<fp16, [1, 77, 1024]> linear_108_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("linear_108_cast")];
            tensor<fp16, []> var_1696_to_fp16 = const()[name = tensor<string, []>("op_1696_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_113_cast = mul(x = linear_108_cast, y = var_1696_to_fp16)[name = tensor<string, []>("tensor_113_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158469376))), lut = tensor<fp16, [4]>([-0x1.594p-6, -0x1.9ccp-8, 0x1.9bcp-8, 0x1.58cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158731584)))];
            tensor<fp16, [1, 77, 1024]> linear_109_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("linear_109_cast")];
            tensor<int32, [4]> var_1701 = const()[name = tensor<string, []>("op_1701"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1702_cast = reshape(shape = var_1701, x = linear_109_cast)[name = tensor<string, []>("op_1702_cast")];
            tensor<int32, [4]> var_1703_perm_0 = const()[name = tensor<string, []>("op_1703_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158733696))), lut = tensor<fp16, [4]>([-0x1.288p-6, -0x1.5ecp-8, 0x1.67p-8, 0x1.2ap-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158995904)))];
            tensor<fp16, [1, 77, 1024]> linear_110_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_109_cast)[name = tensor<string, []>("linear_110_cast")];
            tensor<int32, [4]> var_1708 = const()[name = tensor<string, []>("op_1708"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1709_cast = reshape(shape = var_1708, x = linear_110_cast)[name = tensor<string, []>("op_1709_cast")];
            tensor<int32, [4]> var_1710_perm_0 = const()[name = tensor<string, []>("op_1710_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1717 = const()[name = tensor<string, []>("op_1717"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1718_cast = reshape(shape = var_1717, x = tensor_113_cast)[name = tensor<string, []>("op_1718_cast")];
            tensor<int32, [4]> var_1719_perm_0 = const()[name = tensor<string, []>("op_1719_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1721 = const()[name = tensor<string, []>("op_1721"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_24 = transpose(perm = var_1719_perm_0, x = var_1718_cast)[name = tensor<string, []>("transpose_24")];
            tensor<fp16, [16, 77, 64]> query_states_37_cast = reshape(shape = var_1721, x = transpose_24)[name = tensor<string, []>("query_states_37_cast")];
            tensor<int32, [3]> var_1723 = const()[name = tensor<string, []>("op_1723"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_23 = transpose(perm = var_1703_perm_0, x = var_1702_cast)[name = tensor<string, []>("transpose_23")];
            tensor<fp16, [16, 77, 64]> key_states_75_cast = reshape(shape = var_1723, x = transpose_23)[name = tensor<string, []>("key_states_75_cast")];
            tensor<int32, [3]> var_1725 = const()[name = tensor<string, []>("op_1725"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_22 = transpose(perm = var_1710_perm_0, x = var_1709_cast)[name = tensor<string, []>("transpose_22")];
            tensor<fp16, [16, 77, 64]> value_states_75_cast = reshape(shape = var_1725, x = transpose_22)[name = tensor<string, []>("value_states_75_cast")];
            tensor<int32, [3]> var_1728_perm_0 = const()[name = tensor<string, []>("op_1728_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_109_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_109_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_109_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_109_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_21 = transpose(perm = var_1728_perm_0, x = key_states_75_cast)[name = tensor<string, []>("transpose_21")];
            tensor<fp16, [16, 77, 77]> attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = query_states_37_cast, y = transpose_21)[name = tensor<string, []>("attn_weights_109_cast")];
            tensor<int32, [4]> var_1730 = const()[name = tensor<string, []>("op_1730"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1731_cast = reshape(shape = var_1730, x = attn_weights_109_cast)[name = tensor<string, []>("op_1731_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_111_cast = add(x = var_1731_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_111_cast")];
            tensor<int32, [3]> var_1736 = const()[name = tensor<string, []>("op_1736"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_293_cast = reshape(shape = var_1736, x = attn_weights_111_cast)[name = tensor<string, []>("input_293_cast")];
            tensor<fp16, [16, 77, 77]> input_295_cast = softmax(axis = var_5, x = input_293_cast)[name = tensor<string, []>("input_295_cast")];
            tensor<bool, []> attn_output_109_transpose_x_0 = const()[name = tensor<string, []>("attn_output_109_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_109_transpose_y_0 = const()[name = tensor<string, []>("attn_output_109_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_109_cast = matmul(transpose_x = attn_output_109_transpose_x_0, transpose_y = attn_output_109_transpose_y_0, x = input_295_cast, y = value_states_75_cast)[name = tensor<string, []>("attn_output_109_cast")];
            tensor<int32, [4]> var_1741 = const()[name = tensor<string, []>("op_1741"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_111_cast = reshape(shape = var_1741, x = attn_output_109_cast)[name = tensor<string, []>("attn_output_111_cast")];
            tensor<int32, [4]> attn_output_113_perm_0 = const()[name = tensor<string, []>("attn_output_113_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1744 = const()[name = tensor<string, []>("op_1744"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_20 = transpose(perm = attn_output_113_perm_0, x = attn_output_111_cast)[name = tensor<string, []>("transpose_20")];
            tensor<fp16, [1, 77, 1024]> input_297_cast = reshape(shape = var_1744, x = transpose_20)[name = tensor<string, []>("input_297_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158998016))), lut = tensor<fp16, [4]>([-0x1.27cp-6, -0x1.628p-8, 0x1.6p-8, 0x1.26cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159260224)))];
            tensor<fp16, [1, 77, 1024]> linear_111_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16_palettized, x = input_297_cast)[name = tensor<string, []>("linear_111_cast")];
            tensor<fp16, [1, 77, 1024]> input_299_cast = add(x = input_291_cast, y = linear_111_cast)[name = tensor<string, []>("input_299_cast")];
            tensor<int32, [1]> input_301_axes_0 = const()[name = tensor<string, []>("input_301_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159262336)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159264448)))];
            tensor<fp16, [1, 77, 1024]> input_301_cast = layer_norm(axes = input_301_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16, x = input_299_cast)[name = tensor<string, []>("input_301_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159266560))), lut = tensor<fp16, [4]>([-0x1.37p-6, -0x1.6fp-8, 0x1.7e4p-8, 0x1.3bp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160315200))), lut = tensor<fp16, [4]>([-0x1.13cp-1, -0x1.5b8p-2, 0x1.01cp-2, -0x1.b94p-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_112_cast = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16_palettized, x = input_301_cast)[name = tensor<string, []>("linear_112_cast")];
            tensor<string, []> input_305_mode_0 = const()[name = tensor<string, []>("input_305_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_305_cast = gelu(mode = input_305_mode_0, x = linear_112_cast)[name = tensor<string, []>("input_305_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160316288))), lut = tensor<fp16, [4]>([-0x1.13cp-6, -0x1.4bp-8, 0x1.48cp-8, 0x1.138p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161364928)))];
            tensor<fp16, [1, 77, 1024]> linear_113_cast = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16_palettized, x = input_305_cast)[name = tensor<string, []>("linear_113_cast")];
            tensor<fp16, [1, 77, 1024]> input_307_cast = add(x = input_299_cast, y = linear_113_cast)[name = tensor<string, []>("input_307_cast")];
            tensor<int32, [1]> hidden_states_115_axes_0 = const()[name = tensor<string, []>("hidden_states_115_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161367040)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161369152)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_115_cast = layer_norm(axes = hidden_states_115_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16, x = input_307_cast)[name = tensor<string, []>("hidden_states_115_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161371264))), lut = tensor<fp16, [4]>([-0x1.57cp-6, -0x1.9a8p-8, 0x1.994p-8, 0x1.56cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161633472)))];
            tensor<fp16, [1, 77, 1024]> linear_114_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("linear_114_cast")];
            tensor<fp16, []> var_1783_to_fp16 = const()[name = tensor<string, []>("op_1783_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_119_cast = mul(x = linear_114_cast, y = var_1783_to_fp16)[name = tensor<string, []>("tensor_119_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161635584))), lut = tensor<fp16, [4]>([-0x1.57p-6, -0x1.974p-8, 0x1.9bp-8, 0x1.58p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161897792)))];
            tensor<fp16, [1, 77, 1024]> linear_115_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("linear_115_cast")];
            tensor<int32, [4]> var_1788 = const()[name = tensor<string, []>("op_1788"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1789_cast = reshape(shape = var_1788, x = linear_115_cast)[name = tensor<string, []>("op_1789_cast")];
            tensor<int32, [4]> var_1790_perm_0 = const()[name = tensor<string, []>("op_1790_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161899904))), lut = tensor<fp16, [4]>([-0x1.2fp-6, -0x1.67cp-8, 0x1.69cp-8, 0x1.2fp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162162112)))];
            tensor<fp16, [1, 77, 1024]> linear_116_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_115_cast)[name = tensor<string, []>("linear_116_cast")];
            tensor<int32, [4]> var_1795 = const()[name = tensor<string, []>("op_1795"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1796_cast = reshape(shape = var_1795, x = linear_116_cast)[name = tensor<string, []>("op_1796_cast")];
            tensor<int32, [4]> var_1797_perm_0 = const()[name = tensor<string, []>("op_1797_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1804 = const()[name = tensor<string, []>("op_1804"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1805_cast = reshape(shape = var_1804, x = tensor_119_cast)[name = tensor<string, []>("op_1805_cast")];
            tensor<int32, [4]> var_1806_perm_0 = const()[name = tensor<string, []>("op_1806_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1808 = const()[name = tensor<string, []>("op_1808"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_19 = transpose(perm = var_1806_perm_0, x = var_1805_cast)[name = tensor<string, []>("transpose_19")];
            tensor<fp16, [16, 77, 64]> query_states_39_cast = reshape(shape = var_1808, x = transpose_19)[name = tensor<string, []>("query_states_39_cast")];
            tensor<int32, [3]> var_1810 = const()[name = tensor<string, []>("op_1810"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_18 = transpose(perm = var_1790_perm_0, x = var_1789_cast)[name = tensor<string, []>("transpose_18")];
            tensor<fp16, [16, 77, 64]> key_states_79_cast = reshape(shape = var_1810, x = transpose_18)[name = tensor<string, []>("key_states_79_cast")];
            tensor<int32, [3]> var_1812 = const()[name = tensor<string, []>("op_1812"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_17 = transpose(perm = var_1797_perm_0, x = var_1796_cast)[name = tensor<string, []>("transpose_17")];
            tensor<fp16, [16, 77, 64]> value_states_79_cast = reshape(shape = var_1812, x = transpose_17)[name = tensor<string, []>("value_states_79_cast")];
            tensor<int32, [3]> var_1815_perm_0 = const()[name = tensor<string, []>("op_1815_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_115_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_115_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_115_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_115_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_16 = transpose(perm = var_1815_perm_0, x = key_states_79_cast)[name = tensor<string, []>("transpose_16")];
            tensor<fp16, [16, 77, 77]> attn_weights_115_cast = matmul(transpose_x = attn_weights_115_transpose_x_0, transpose_y = attn_weights_115_transpose_y_0, x = query_states_39_cast, y = transpose_16)[name = tensor<string, []>("attn_weights_115_cast")];
            tensor<int32, [4]> var_1817 = const()[name = tensor<string, []>("op_1817"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1818_cast = reshape(shape = var_1817, x = attn_weights_115_cast)[name = tensor<string, []>("op_1818_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_117_cast = add(x = var_1818_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_117_cast")];
            tensor<int32, [3]> var_1823 = const()[name = tensor<string, []>("op_1823"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_309_cast = reshape(shape = var_1823, x = attn_weights_117_cast)[name = tensor<string, []>("input_309_cast")];
            tensor<fp16, [16, 77, 77]> input_311_cast = softmax(axis = var_5, x = input_309_cast)[name = tensor<string, []>("input_311_cast")];
            tensor<bool, []> attn_output_115_transpose_x_0 = const()[name = tensor<string, []>("attn_output_115_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_115_transpose_y_0 = const()[name = tensor<string, []>("attn_output_115_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_115_cast = matmul(transpose_x = attn_output_115_transpose_x_0, transpose_y = attn_output_115_transpose_y_0, x = input_311_cast, y = value_states_79_cast)[name = tensor<string, []>("attn_output_115_cast")];
            tensor<int32, [4]> var_1828 = const()[name = tensor<string, []>("op_1828"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_117_cast = reshape(shape = var_1828, x = attn_output_115_cast)[name = tensor<string, []>("attn_output_117_cast")];
            tensor<int32, [4]> attn_output_119_perm_0 = const()[name = tensor<string, []>("attn_output_119_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1831 = const()[name = tensor<string, []>("op_1831"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_15 = transpose(perm = attn_output_119_perm_0, x = attn_output_117_cast)[name = tensor<string, []>("transpose_15")];
            tensor<fp16, [1, 77, 1024]> input_313_cast = reshape(shape = var_1831, x = transpose_15)[name = tensor<string, []>("input_313_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162164224))), lut = tensor<fp16, [4]>([-0x1.2ep-6, -0x1.688p-8, 0x1.698p-8, 0x1.2ep-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162426432)))];
            tensor<fp16, [1, 77, 1024]> linear_117_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16_palettized, x = input_313_cast)[name = tensor<string, []>("linear_117_cast")];
            tensor<fp16, [1, 77, 1024]> input_315_cast = add(x = input_307_cast, y = linear_117_cast)[name = tensor<string, []>("input_315_cast")];
            tensor<int32, [1]> input_317_axes_0 = const()[name = tensor<string, []>("input_317_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162428544)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162430656)))];
            tensor<fp16, [1, 77, 1024]> input_317_cast = layer_norm(axes = input_317_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16, x = input_315_cast)[name = tensor<string, []>("input_317_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162432768))), lut = tensor<fp16, [4]>([-0x1.348p-6, -0x1.688p-8, 0x1.83p-8, 0x1.3b8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163481408))), lut = tensor<fp16, [4]>([-0x1.5d4p-2, -0x1.294p-1, -0x1.c6p-2, 0x1.b4cp-3]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_118_cast = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16_palettized, x = input_317_cast)[name = tensor<string, []>("linear_118_cast")];
            tensor<string, []> input_321_mode_0 = const()[name = tensor<string, []>("input_321_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_321_cast = gelu(mode = input_321_mode_0, x = linear_118_cast)[name = tensor<string, []>("input_321_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163482496))), lut = tensor<fp16, [4]>([-0x1.16p-6, -0x1.4d4p-8, 0x1.4cp-8, 0x1.16p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164531136)))];
            tensor<fp16, [1, 77, 1024]> linear_119_cast = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16_palettized, x = input_321_cast)[name = tensor<string, []>("linear_119_cast")];
            tensor<fp16, [1, 77, 1024]> input_323_cast = add(x = input_315_cast, y = linear_119_cast)[name = tensor<string, []>("input_323_cast")];
            tensor<int32, [1]> hidden_states_121_axes_0 = const()[name = tensor<string, []>("hidden_states_121_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164533248)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164535360)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_121_cast = layer_norm(axes = hidden_states_121_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16, x = input_323_cast)[name = tensor<string, []>("hidden_states_121_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164537472))), lut = tensor<fp16, [4]>([-0x1.54p-6, -0x1.974p-8, 0x1.938p-8, 0x1.534p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164799680)))];
            tensor<fp16, [1, 77, 1024]> linear_120_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("linear_120_cast")];
            tensor<fp16, []> var_1870_to_fp16 = const()[name = tensor<string, []>("op_1870_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_125_cast = mul(x = linear_120_cast, y = var_1870_to_fp16)[name = tensor<string, []>("tensor_125_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164801792))), lut = tensor<fp16, [4]>([-0x1.554p-6, -0x1.97p-8, 0x1.954p-8, 0x1.54cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165064000)))];
            tensor<fp16, [1, 77, 1024]> linear_121_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("linear_121_cast")];
            tensor<int32, [4]> var_1875 = const()[name = tensor<string, []>("op_1875"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1876_cast = reshape(shape = var_1875, x = linear_121_cast)[name = tensor<string, []>("op_1876_cast")];
            tensor<int32, [4]> var_1877_perm_0 = const()[name = tensor<string, []>("op_1877_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165066112))), lut = tensor<fp16, [4]>([-0x1.2d4p-6, -0x1.688p-8, 0x1.62cp-8, 0x1.2b4p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165328320)))];
            tensor<fp16, [1, 77, 1024]> linear_122_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_121_cast)[name = tensor<string, []>("linear_122_cast")];
            tensor<int32, [4]> var_1882 = const()[name = tensor<string, []>("op_1882"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1883_cast = reshape(shape = var_1882, x = linear_122_cast)[name = tensor<string, []>("op_1883_cast")];
            tensor<int32, [4]> var_1884_perm_0 = const()[name = tensor<string, []>("op_1884_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1891 = const()[name = tensor<string, []>("op_1891"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1892_cast = reshape(shape = var_1891, x = tensor_125_cast)[name = tensor<string, []>("op_1892_cast")];
            tensor<int32, [4]> var_1893_perm_0 = const()[name = tensor<string, []>("op_1893_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1895 = const()[name = tensor<string, []>("op_1895"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_14 = transpose(perm = var_1893_perm_0, x = var_1892_cast)[name = tensor<string, []>("transpose_14")];
            tensor<fp16, [16, 77, 64]> query_states_41_cast = reshape(shape = var_1895, x = transpose_14)[name = tensor<string, []>("query_states_41_cast")];
            tensor<int32, [3]> var_1897 = const()[name = tensor<string, []>("op_1897"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_13 = transpose(perm = var_1877_perm_0, x = var_1876_cast)[name = tensor<string, []>("transpose_13")];
            tensor<fp16, [16, 77, 64]> key_states_83_cast = reshape(shape = var_1897, x = transpose_13)[name = tensor<string, []>("key_states_83_cast")];
            tensor<int32, [3]> var_1899 = const()[name = tensor<string, []>("op_1899"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_12 = transpose(perm = var_1884_perm_0, x = var_1883_cast)[name = tensor<string, []>("transpose_12")];
            tensor<fp16, [16, 77, 64]> value_states_83_cast = reshape(shape = var_1899, x = transpose_12)[name = tensor<string, []>("value_states_83_cast")];
            tensor<int32, [3]> var_1902_perm_0 = const()[name = tensor<string, []>("op_1902_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_121_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_121_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_121_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_121_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_11 = transpose(perm = var_1902_perm_0, x = key_states_83_cast)[name = tensor<string, []>("transpose_11")];
            tensor<fp16, [16, 77, 77]> attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = query_states_41_cast, y = transpose_11)[name = tensor<string, []>("attn_weights_121_cast")];
            tensor<int32, [4]> var_1904 = const()[name = tensor<string, []>("op_1904"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1905_cast = reshape(shape = var_1904, x = attn_weights_121_cast)[name = tensor<string, []>("op_1905_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_123_cast = add(x = var_1905_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_123_cast")];
            tensor<int32, [3]> var_1910 = const()[name = tensor<string, []>("op_1910"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_325_cast = reshape(shape = var_1910, x = attn_weights_123_cast)[name = tensor<string, []>("input_325_cast")];
            tensor<fp16, [16, 77, 77]> input_327_cast = softmax(axis = var_5, x = input_325_cast)[name = tensor<string, []>("input_327_cast")];
            tensor<bool, []> attn_output_121_transpose_x_0 = const()[name = tensor<string, []>("attn_output_121_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_121_transpose_y_0 = const()[name = tensor<string, []>("attn_output_121_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_121_cast = matmul(transpose_x = attn_output_121_transpose_x_0, transpose_y = attn_output_121_transpose_y_0, x = input_327_cast, y = value_states_83_cast)[name = tensor<string, []>("attn_output_121_cast")];
            tensor<int32, [4]> var_1915 = const()[name = tensor<string, []>("op_1915"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_123_cast = reshape(shape = var_1915, x = attn_output_121_cast)[name = tensor<string, []>("attn_output_123_cast")];
            tensor<int32, [4]> attn_output_125_perm_0 = const()[name = tensor<string, []>("attn_output_125_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1918 = const()[name = tensor<string, []>("op_1918"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_10 = transpose(perm = attn_output_125_perm_0, x = attn_output_123_cast)[name = tensor<string, []>("transpose_10")];
            tensor<fp16, [1, 77, 1024]> input_329_cast = reshape(shape = var_1918, x = transpose_10)[name = tensor<string, []>("input_329_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165330432))), lut = tensor<fp16, [4]>([-0x1.2bcp-6, -0x1.648p-8, 0x1.67p-8, 0x1.2bcp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165592640)))];
            tensor<fp16, [1, 77, 1024]> linear_123_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16_palettized, x = input_329_cast)[name = tensor<string, []>("linear_123_cast")];
            tensor<fp16, [1, 77, 1024]> input_331_cast = add(x = input_323_cast, y = linear_123_cast)[name = tensor<string, []>("input_331_cast")];
            tensor<int32, [1]> input_333_axes_0 = const()[name = tensor<string, []>("input_333_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165594752)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165596864)))];
            tensor<fp16, [1, 77, 1024]> input_333_cast = layer_norm(axes = input_333_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16, x = input_331_cast)[name = tensor<string, []>("input_333_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165598976))), lut = tensor<fp16, [4]>([-0x1.35p-6, -0x1.6e4p-8, 0x1.78p-8, 0x1.374p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166647616))), lut = tensor<fp16, [4]>([-0x1.a7cp-2, -0x1.494p-2, -0x1.0acp-1, 0x1.82cp-4]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_124_cast = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16_palettized, x = input_333_cast)[name = tensor<string, []>("linear_124_cast")];
            tensor<string, []> input_337_mode_0 = const()[name = tensor<string, []>("input_337_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_337_cast = gelu(mode = input_337_mode_0, x = linear_124_cast)[name = tensor<string, []>("input_337_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166648704))), lut = tensor<fp16, [4]>([-0x1.17p-6, -0x1.4d4p-8, 0x1.4ecp-8, 0x1.17cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167697344)))];
            tensor<fp16, [1, 77, 1024]> linear_125_cast = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16_palettized, x = input_337_cast)[name = tensor<string, []>("linear_125_cast")];
            tensor<fp16, [1, 77, 1024]> input_339_cast = add(x = input_331_cast, y = linear_125_cast)[name = tensor<string, []>("input_339_cast")];
            tensor<int32, [1]> hidden_states_127_axes_0 = const()[name = tensor<string, []>("hidden_states_127_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167699456)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167701568)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_127_cast = layer_norm(axes = hidden_states_127_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16, x = input_339_cast)[name = tensor<string, []>("hidden_states_127_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167703680))), lut = tensor<fp16, [4]>([-0x1.528p-6, -0x1.94cp-8, 0x1.924p-8, 0x1.518p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167965888)))];
            tensor<fp16, [1, 77, 1024]> linear_126_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("linear_126_cast")];
            tensor<fp16, []> var_1957_to_fp16 = const()[name = tensor<string, []>("op_1957_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_131_cast = mul(x = linear_126_cast, y = var_1957_to_fp16)[name = tensor<string, []>("tensor_131_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167968000))), lut = tensor<fp16, [4]>([-0x1.554p-6, -0x1.96p-8, 0x1.96cp-8, 0x1.54cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168230208)))];
            tensor<fp16, [1, 77, 1024]> linear_127_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("linear_127_cast")];
            tensor<int32, [4]> var_1962 = const()[name = tensor<string, []>("op_1962"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1963_cast = reshape(shape = var_1962, x = linear_127_cast)[name = tensor<string, []>("op_1963_cast")];
            tensor<int32, [4]> var_1964_perm_0 = const()[name = tensor<string, []>("op_1964_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168232320))), lut = tensor<fp16, [4]>([-0x1.24cp-6, -0x1.5c4p-8, 0x1.5e4p-8, 0x1.25p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168494528)))];
            tensor<fp16, [1, 77, 1024]> linear_128_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor<string, []>("linear_128_cast")];
            tensor<int32, [4]> var_1969 = const()[name = tensor<string, []>("op_1969"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1970_cast = reshape(shape = var_1969, x = linear_128_cast)[name = tensor<string, []>("op_1970_cast")];
            tensor<int32, [4]> var_1971_perm_0 = const()[name = tensor<string, []>("op_1971_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_1978 = const()[name = tensor<string, []>("op_1978"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_1979_cast = reshape(shape = var_1978, x = tensor_131_cast)[name = tensor<string, []>("op_1979_cast")];
            tensor<int32, [4]> var_1980_perm_0 = const()[name = tensor<string, []>("op_1980_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_1982 = const()[name = tensor<string, []>("op_1982"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_9 = transpose(perm = var_1980_perm_0, x = var_1979_cast)[name = tensor<string, []>("transpose_9")];
            tensor<fp16, [16, 77, 64]> query_states_43_cast = reshape(shape = var_1982, x = transpose_9)[name = tensor<string, []>("query_states_43_cast")];
            tensor<int32, [3]> var_1984 = const()[name = tensor<string, []>("op_1984"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_8 = transpose(perm = var_1964_perm_0, x = var_1963_cast)[name = tensor<string, []>("transpose_8")];
            tensor<fp16, [16, 77, 64]> key_states_87_cast = reshape(shape = var_1984, x = transpose_8)[name = tensor<string, []>("key_states_87_cast")];
            tensor<int32, [3]> var_1986 = const()[name = tensor<string, []>("op_1986"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_7 = transpose(perm = var_1971_perm_0, x = var_1970_cast)[name = tensor<string, []>("transpose_7")];
            tensor<fp16, [16, 77, 64]> value_states_87_cast = reshape(shape = var_1986, x = transpose_7)[name = tensor<string, []>("value_states_87_cast")];
            tensor<int32, [3]> var_1989_perm_0 = const()[name = tensor<string, []>("op_1989_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_127_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_127_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_127_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_127_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_6 = transpose(perm = var_1989_perm_0, x = key_states_87_cast)[name = tensor<string, []>("transpose_6")];
            tensor<fp16, [16, 77, 77]> attn_weights_127_cast = matmul(transpose_x = attn_weights_127_transpose_x_0, transpose_y = attn_weights_127_transpose_y_0, x = query_states_43_cast, y = transpose_6)[name = tensor<string, []>("attn_weights_127_cast")];
            tensor<int32, [4]> var_1991 = const()[name = tensor<string, []>("op_1991"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_1992_cast = reshape(shape = var_1991, x = attn_weights_127_cast)[name = tensor<string, []>("op_1992_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_129_cast = add(x = var_1992_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_129_cast")];
            tensor<int32, [3]> var_1997 = const()[name = tensor<string, []>("op_1997"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_341_cast = reshape(shape = var_1997, x = attn_weights_129_cast)[name = tensor<string, []>("input_341_cast")];
            tensor<fp16, [16, 77, 77]> input_343_cast = softmax(axis = var_5, x = input_341_cast)[name = tensor<string, []>("input_343_cast")];
            tensor<bool, []> attn_output_127_transpose_x_0 = const()[name = tensor<string, []>("attn_output_127_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_127_transpose_y_0 = const()[name = tensor<string, []>("attn_output_127_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_127_cast = matmul(transpose_x = attn_output_127_transpose_x_0, transpose_y = attn_output_127_transpose_y_0, x = input_343_cast, y = value_states_87_cast)[name = tensor<string, []>("attn_output_127_cast")];
            tensor<int32, [4]> var_2002 = const()[name = tensor<string, []>("op_2002"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_129_cast = reshape(shape = var_2002, x = attn_output_127_cast)[name = tensor<string, []>("attn_output_129_cast")];
            tensor<int32, [4]> attn_output_131_perm_0 = const()[name = tensor<string, []>("attn_output_131_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2005 = const()[name = tensor<string, []>("op_2005"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_5 = transpose(perm = attn_output_131_perm_0, x = attn_output_129_cast)[name = tensor<string, []>("transpose_5")];
            tensor<fp16, [1, 77, 1024]> input_345_cast = reshape(shape = var_2005, x = transpose_5)[name = tensor<string, []>("input_345_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168496640))), lut = tensor<fp16, [4]>([-0x1.248p-6, -0x1.5dcp-8, 0x1.5dcp-8, 0x1.25p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168758848)))];
            tensor<fp16, [1, 77, 1024]> linear_129_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16_palettized, x = input_345_cast)[name = tensor<string, []>("linear_129_cast")];
            tensor<fp16, [1, 77, 1024]> input_347_cast = add(x = input_339_cast, y = linear_129_cast)[name = tensor<string, []>("input_347_cast")];
            tensor<int32, [1]> input_349_axes_0 = const()[name = tensor<string, []>("input_349_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168760960)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168763072)))];
            tensor<fp16, [1, 77, 1024]> input_349_cast = layer_norm(axes = input_349_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16, x = input_347_cast)[name = tensor<string, []>("input_349_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168765184))), lut = tensor<fp16, [4]>([-0x1.344p-6, -0x1.6ep-8, 0x1.76cp-8, 0x1.368p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169813824))), lut = tensor<fp16, [4]>([-0x1.56cp-1, -0x1.948p-2, -0x1.3acp-2, -0x1.eecp-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_130_cast = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16_palettized, x = input_349_cast)[name = tensor<string, []>("linear_130_cast")];
            tensor<string, []> input_353_mode_0 = const()[name = tensor<string, []>("input_353_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_353_cast = gelu(mode = input_353_mode_0, x = linear_130_cast)[name = tensor<string, []>("input_353_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169814912))), lut = tensor<fp16, [4]>([-0x1.144p-6, -0x1.4a8p-8, 0x1.49p-8, 0x1.13cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170863552)))];
            tensor<fp16, [1, 77, 1024]> linear_131_cast = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16_palettized, x = input_353_cast)[name = tensor<string, []>("linear_131_cast")];
            tensor<fp16, [1, 77, 1024]> input_355_cast = add(x = input_347_cast, y = linear_131_cast)[name = tensor<string, []>("input_355_cast")];
            tensor<int32, [1]> hidden_states_133_axes_0 = const()[name = tensor<string, []>("hidden_states_133_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170865664)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170867776)))];
            tensor<fp16, [1, 77, 1024]> hidden_states_133_cast = layer_norm(axes = hidden_states_133_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16, x = input_355_cast)[name = tensor<string, []>("hidden_states_133_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170869888))), lut = tensor<fp16, [4]>([-0x1.4d8p-6, -0x1.8f4p-8, 0x1.8b4p-8, 0x1.4c8p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171132096)))];
            tensor<fp16, [1, 77, 1024]> linear_132_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16_palettized, x = hidden_states_133_cast)[name = tensor<string, []>("linear_132_cast")];
            tensor<fp16, []> var_2044_to_fp16 = const()[name = tensor<string, []>("op_2044_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [1, 77, 1024]> tensor_cast = mul(x = linear_132_cast, y = var_2044_to_fp16)[name = tensor<string, []>("tensor_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171134208))), lut = tensor<fp16, [4]>([-0x1.4ecp-6, -0x1.8d8p-8, 0x1.918p-8, 0x1.4fcp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171396416)))];
            tensor<fp16, [1, 77, 1024]> linear_133_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16_palettized, x = hidden_states_133_cast)[name = tensor<string, []>("linear_133_cast")];
            tensor<int32, [4]> var_2049 = const()[name = tensor<string, []>("op_2049"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_2050_cast = reshape(shape = var_2049, x = linear_133_cast)[name = tensor<string, []>("op_2050_cast")];
            tensor<int32, [4]> var_2051_perm_0 = const()[name = tensor<string, []>("op_2051_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171398528))), lut = tensor<fp16, [4]>([-0x1.26cp-6, -0x1.5fp-8, 0x1.604p-8, 0x1.264p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171660736)))];
            tensor<fp16, [1, 77, 1024]> linear_134_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16_palettized, x = hidden_states_133_cast)[name = tensor<string, []>("linear_134_cast")];
            tensor<int32, [4]> var_2056 = const()[name = tensor<string, []>("op_2056"), val = tensor<int32, [4]>([1, -1, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_2057_cast = reshape(shape = var_2056, x = linear_134_cast)[name = tensor<string, []>("op_2057_cast")];
            tensor<int32, [4]> var_2058_perm_0 = const()[name = tensor<string, []>("op_2058_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [4]> var_2065 = const()[name = tensor<string, []>("op_2065"), val = tensor<int32, [4]>([1, 77, 16, 64])];
            tensor<fp16, [1, 77, 16, 64]> var_2066_cast = reshape(shape = var_2065, x = tensor_cast)[name = tensor<string, []>("op_2066_cast")];
            tensor<int32, [4]> var_2067_perm_0 = const()[name = tensor<string, []>("op_2067_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2069 = const()[name = tensor<string, []>("op_2069"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_4 = transpose(perm = var_2067_perm_0, x = var_2066_cast)[name = tensor<string, []>("transpose_4")];
            tensor<fp16, [16, 77, 64]> query_states_cast = reshape(shape = var_2069, x = transpose_4)[name = tensor<string, []>("query_states_cast")];
            tensor<int32, [3]> var_2071 = const()[name = tensor<string, []>("op_2071"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_3 = transpose(perm = var_2051_perm_0, x = var_2050_cast)[name = tensor<string, []>("transpose_3")];
            tensor<fp16, [16, 77, 64]> key_states_cast = reshape(shape = var_2071, x = transpose_3)[name = tensor<string, []>("key_states_cast")];
            tensor<int32, [3]> var_2073 = const()[name = tensor<string, []>("op_2073"), val = tensor<int32, [3]>([16, -1, 64])];
            tensor<fp16, [1, 16, 77, 64]> transpose_2 = transpose(perm = var_2058_perm_0, x = var_2057_cast)[name = tensor<string, []>("transpose_2")];
            tensor<fp16, [16, 77, 64]> value_states_cast = reshape(shape = var_2073, x = transpose_2)[name = tensor<string, []>("value_states_cast")];
            tensor<int32, [3]> var_2076_perm_0 = const()[name = tensor<string, []>("op_2076_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<bool, []> attn_weights_133_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_133_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_weights_133_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_133_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 64, 77]> transpose_1 = transpose(perm = var_2076_perm_0, x = key_states_cast)[name = tensor<string, []>("transpose_1")];
            tensor<fp16, [16, 77, 77]> attn_weights_133_cast = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = query_states_cast, y = transpose_1)[name = tensor<string, []>("attn_weights_133_cast")];
            tensor<int32, [4]> var_2078 = const()[name = tensor<string, []>("op_2078"), val = tensor<int32, [4]>([1, 16, 77, 77])];
            tensor<fp16, [1, 16, 77, 77]> var_2079_cast = reshape(shape = var_2078, x = attn_weights_133_cast)[name = tensor<string, []>("op_2079_cast")];
            tensor<fp16, [1, 16, 77, 77]> attn_weights_135_cast = add(x = var_2079_cast, y = op_57_to_fp16_palettized)[name = tensor<string, []>("attn_weights_135_cast")];
            tensor<int32, [3]> var_2084 = const()[name = tensor<string, []>("op_2084"), val = tensor<int32, [3]>([16, 77, 77])];
            tensor<fp16, [16, 77, 77]> input_357_cast = reshape(shape = var_2084, x = attn_weights_135_cast)[name = tensor<string, []>("input_357_cast")];
            tensor<fp16, [16, 77, 77]> input_359_cast = softmax(axis = var_5, x = input_357_cast)[name = tensor<string, []>("input_359_cast")];
            tensor<bool, []> attn_output_133_transpose_x_0 = const()[name = tensor<string, []>("attn_output_133_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attn_output_133_transpose_y_0 = const()[name = tensor<string, []>("attn_output_133_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [16, 77, 64]> attn_output_133_cast = matmul(transpose_x = attn_output_133_transpose_x_0, transpose_y = attn_output_133_transpose_y_0, x = input_359_cast, y = value_states_cast)[name = tensor<string, []>("attn_output_133_cast")];
            tensor<int32, [4]> var_2089 = const()[name = tensor<string, []>("op_2089"), val = tensor<int32, [4]>([1, 16, 77, 64])];
            tensor<fp16, [1, 16, 77, 64]> attn_output_135_cast = reshape(shape = var_2089, x = attn_output_133_cast)[name = tensor<string, []>("attn_output_135_cast")];
            tensor<int32, [4]> attn_output_perm_0 = const()[name = tensor<string, []>("attn_output_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> var_2092 = const()[name = tensor<string, []>("op_2092"), val = tensor<int32, [3]>([1, 77, 1024])];
            tensor<fp16, [1, 77, 16, 64]> transpose_0 = transpose(perm = attn_output_perm_0, x = attn_output_135_cast)[name = tensor<string, []>("transpose_0")];
            tensor<fp16, [1, 77, 1024]> input_361_cast = reshape(shape = var_2092, x = transpose_0)[name = tensor<string, []>("input_361_cast")];
            tensor<fp16, [1024, 1024]> text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171662848))), lut = tensor<fp16, [4]>([-0x1.26cp-6, -0x1.608p-8, 0x1.614p-8, 0x1.27p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 1024])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171925056)))];
            tensor<fp16, [1, 77, 1024]> linear_135_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16_palettized, x = input_361_cast)[name = tensor<string, []>("linear_135_cast")];
            tensor<fp16, [1, 77, 1024]> input_363_cast = add(x = input_355_cast, y = linear_135_cast)[name = tensor<string, []>("input_363_cast")];
            tensor<int32, [1]> input_365_axes_0 = const()[name = tensor<string, []>("input_365_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171927168)))];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171929280)))];
            tensor<fp16, [1, 77, 1024]> input_365_cast = layer_norm(axes = input_365_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16, x = input_363_cast)[name = tensor<string, []>("input_365_cast")];
            tensor<fp16, [4096, 1024]> text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171931392))), lut = tensor<fp16, [4]>([-0x1.358p-6, -0x1.6d4p-8, 0x1.7cp-8, 0x1.39cp-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([4096, 1024])];
            tensor<fp16, [4096]> text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172980032))), lut = tensor<fp16, [4]>([-0x1.e28p-2, -0x1.154p-2, -0x1.41cp-1, -0x1.7e8p-2]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16_palettized"), shape = tensor<uint32, [1]>([4096])];
            tensor<fp16, [1, 77, 4096]> linear_136_cast = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16_palettized, weight = text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16_palettized, x = input_365_cast)[name = tensor<string, []>("linear_136_cast")];
            tensor<string, []> input_369_mode_0 = const()[name = tensor<string, []>("input_369_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [1, 77, 4096]> input_369_cast = gelu(mode = input_369_mode_0, x = linear_136_cast)[name = tensor<string, []>("input_369_cast")];
            tensor<fp16, [1024, 4096]> text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1048576]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172981120))), lut = tensor<fp16, [4]>([-0x1.114p-6, -0x1.45p-8, 0x1.47cp-8, 0x1.12p-6]), name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 4096])];
            tensor<fp16, [1024]> text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174029760)))];
            tensor<fp16, [1, 77, 1024]> linear_137_cast = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16_palettized, x = input_369_cast)[name = tensor<string, []>("linear_137_cast")];
            tensor<fp16, [1, 77, 1024]> input_cast = add(x = input_363_cast, y = linear_137_cast)[name = tensor<string, []>("input_cast")];
            tensor<int32, [1]> last_hidden_state_axes_0 = const()[name = tensor<string, []>("last_hidden_state_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1024]> text_encoder_text_model_final_layer_norm_weight_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_final_layer_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174031872)))];
            tensor<fp16, [1024]> text_encoder_text_model_final_layer_norm_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_text_model_final_layer_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174033984)))];
            tensor<fp16, [1, 77, 1024]> last_hidden_state_cast = layer_norm(axes = last_hidden_state_axes_0, beta = text_encoder_text_model_final_layer_norm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_final_layer_norm_weight_to_fp16, x = input_cast)[name = tensor<string, []>("last_hidden_state_cast")];
            tensor<string, []> last_hidden_state_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("last_hidden_state_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, [1]> var_2117 = const()[name = tensor<string, []>("op_2117"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> var_2119 = reduce_argmax(axis = var_5, keep_dims = var_6, x = cast_2)[name = tensor<string, []>("op_2119")];
            tensor<int32, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(1)];
            tensor<int32, [1, 2]> stack_0 = stack(axis = stack_0_axis_0, values = (var_2117, var_2119))[name = tensor<string, []>("stack_0")];
            tensor<int32, []> var_2121_transpose_batch_dims_0 = const()[name = tensor<string, []>("op_2121_transpose_batch_dims_0"), val = tensor<int32, []>(0)];
            tensor<fp16, [1, 1024]> var_2121_transpose_cast = gather_nd(batch_dims = var_2121_transpose_batch_dims_0, indices = stack_0, x = last_hidden_state_cast)[name = tensor<string, []>("op_2121_transpose_cast")];
            tensor<string, []> var_2121_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_2121_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 77, 1024]> last_hidden_state = cast(dtype = last_hidden_state_cast_to_fp32_dtype_0, x = last_hidden_state_cast)[name = tensor<string, []>("cast_0")];
            tensor<fp32, [1, 1024]> pooled_outputs = cast(dtype = var_2121_cast_to_fp32_dtype_0, x = var_2121_transpose_cast)[name = tensor<string, []>("cast_1")];
        } -> (last_hidden_state, pooled_outputs);
}