I found there seems to be one additional parameter in the torch.nn.Model for convnet as shown in the example below. And the shape the additional parameter seems like [in_channel, out_channl]. Can anyone help explain this?
Thanks in advance!
conv1 = torch.nn.Conv2d(1, 2, 1)
print("conv1:", conv1.weight)

class myModel(torch.nn.Module):
    def __init__(self):
        super(myModel, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 2, 1)
    def forward(self, x):
        return torch.nn.functional.conv2d(x, self.filter)

model = myModel()# .cuda()
print("model:", [i for i in model.parameters()])

The output or first print is:
conv1: Parameter containing:
tensor([[[[-0.6750]]],
        [[[ 0.2480]]]], requires_grad=True)

The output of second print is:
model: [Parameter containing:
tensor([[[[ 0.9591]]],
        [[[-0.9882]]]], requires_grad=True), Parameter containing:
tensor([0.3482, 0.2676], requires_grad=True)]