import torch.onnx 

#Function to Convert to ONNX 
def Convert_ONNX(): 

    # set the model to inference 1mode 2
    model.eval() 

    # Let's create a dummy input tensor  
    dummy_input = torch.randn(1, input_size, requires_grad=True)  

    # Export the model   
    torch.onnx.export(model,         # model being run 
         dummy_input,       # model input (or a tuple for multiple inputs) 
         "ImageClassifier.onnx",       # where to save the model  
         export_params=True,  # store the trained parameter weights inside the model file 
         opset_version=10,    # the ONNX version to export the model to 
         do_constant_folding=True,  # whether to execute constant folding for optimization 
         input_names = ['modelInput'],   # the model's input names 
         output_names = ['modelOutput'], # the model's output names 
         dynamic_axes={'modelInput' : {0 : 'batch_size'},    # variable length axes 
                                'modelOutput' : {0 : 'batch_size'}}) 
    print(" ") 
    print('Model has been converted to ONNX')