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#Let us load the weights for the Convolutional layers
# we are loading weights as common beacuse when batchnormalization is present there is no bias for conv layer
num_weights = conv.weight.numel()
   
#Do the same as above for weights
conv_weights = torch.from_numpy(weights[ptr:ptr+num_weights])
ptr = ptr + num_weights
conv_weights = conv_weights.view_as(conv.weight.data)
conv.weight.data.copy_(conv_weights)
# Note: we dont have bias for conv when batch normalization is there