I think this statement points towards reusing (trained) filters from convolutions in transposed convolutions at a later stage in the model:
conv = nn.Conv2d(3, 6, 3, 1, 1, bias=False)
x = torch.randn(1, 3, 24, 24)
out = conv(x)
out = F.conv_transpose2d(out, conv.weight, stride=1, padding=1)
print(out.shape)
> torch.Size([1, 3, 24, 24])