I am trying to understand an example snippet that makes use of the PyTorch transposed convolution function, with documentation here, where in the docs the author writes:

“The padding argument effectively adds dilation * (kernel_size - 1) -
padding amount of zero padding to both sizes of the input.”

Consider the snippet below where a [1, 1, 4, 4] sample image of all ones is input
to a ConvTranspose2D operation with arguments stride=2 and padding=1 with a weight matrix of shape [1, 1, 4, 4] that has entries from a range from 1 to 16 (in this case dilation=1, added_padding = 1*(4-1)-1 = 2)
import torch.nn as nn
sample_im = torch.ones(1, 1, 4, 4).cuda()
sample_deconv2 = nn.ConvTranspose2d(1, 1, 4, 2, 1, bias=False).cuda()
sample_deconv2.weight = torch.nn.Parameter(torch.tensor([[[[1., 2., 3., 4.], [5., 6., 7., 8.], [9., 10., 11., 12.], [13., 14., 15., 16.]]]]).cuda())
x = sample_deconv2(sample_im)
print(x)

Which yields:
tensor([[[[ 6., 12., 14., 12., 14., 12., 14.,  7.],
          [12., 24., 28., 24., 28., 24., 28., 14.],
          [20., 40., 44., 40., 44., 40., 44., 22.],
          [12., 24., 28., 24., 28., 24., 28., 14.],
          [20., 40., 44., 40., 44., 40., 44., 22.],
          [12., 24., 28., 24., 28., 24., 28., 14.],
          [20., 40., 44., 40., 44., 40., 44., 22.],
          [10., 20., 22., 20., 22., 20., 22., 11.]]]], device='cuda:0',
       grad_fn=<CudnnConvolutionTransposeBackward>)

Now I have seen simple examples of transposed convolution without stride and padding, for instance if the input is a 2x2 image  [[2, 4], [0, 1]], and the convolutional filter with one output channel is [[3, 1], [1, 5]], then the resulting tensor of shape [1, 1, 3, 3] can be seen as the sum of the rightmost 4 matrices in the image below:

The problem is I can’t seem to find examples that use striding or padding in the same visualization. As per my snippet, I am having a very difficult time understanding how the padding is applied to the sample image, or how the stride works to get this output. Any insights appreciated, even just understanding how the 6 in the (0,0) entry or the 12 in the (0,1) entry of the resulting matrix are computed would be very helpful.