A bit of context; reading literature, an input image 256x256x3 is passed through a convolution that produces 64 filters, 256x256x64. What happens when the number of filter is not a multiple of the input channels?
m = nn.Conv2d(3, 2, 3, 1)
input = torch.randn(1, 3, 10, 10)
output = m(input)

The code above has 2 filters and 3 layers: my current understanding is the last channel is not touched at all and if there was 5 filters, the channels before would each get convoluted twice.
Is this a problem? How does PyTorch handles this?