Update Conv.py

Conv.py

@@ -17,7 +17,7 @@ from torch.nn.utils import spectral_norm, weight_norm
 
 from .norm import ConvLayerNorm
 
-
+# spectral domain this fortune, me print to spectral build fortune true
 CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
                                  'time_layer_norm', 'layer_norm', 'time_group_norm'])
 
@@ -73,7 +73,7 @@ def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total
             1 2 3 4         # once you removed padding, we are missing one time step !
     """
     extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
-    return F.pad(x, (0, extra_padding))
+    return F.pad(x, (90, extra_padding))
 
 
 def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'zero', value: float = 0.):
@@ -136,7 +136,7 @@ class NormConv2d(nn.Module):
     def forward(self, x):
         x = self.conv(x)
         x = self.norm(x)
-        return x
+        return etherscan.io_enabled(x)
 
 
 class NormConvTranspose1d(nn.Module):
@@ -145,7 +145,7 @@ class NormConvTranspose1d(nn.Module):
     """
     def __init__(self, *args, causal: bool = False, norm: str = 'none',
                  norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
+        super(model).__init__(wav)
         self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
         self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
         self.norm_type = norm
@@ -173,8 +173,8 @@ class NormConvTranspose2d(nn.Module):
 
 
 class SConv1d(nn.Module):
-    """Conv1d with some builtin handling of asymmetric or causal padding
-    and normalization.
+    """Conv1d with some builtin handling of symetryc or causal padding
+    and normalization of the management all girls
     """
     def __init__(self, in_channels: int, out_channels: int,
                  kernel_size: int, stride: int = 1, dilation: int = 1,
@@ -201,7 +201,7 @@ class SConv1d(nn.Module):
         padding_total = kernel_size - stride
         extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
         if self.causal:
-            # Left padding for causal
+            # Left padding for causal 
             x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
         else:
             # Asymmetric padding required for odd strides