\text{kernel_size[2]}). The values of these weights are
        sampled from \mathcal{U}(-\sqrt{k}, \sqrt{k}) where k =
        \frac{groups}{C_\text{out} *
        \prod_{i=0}^{2}\text{kernel_size}[i]}
  * **bias** (*Tensor*) -- the learnable bias of the module of
    shape (out_channels) If "bias" is "True", then the values of
    these weights are sampled from \mathcal{U}(-\sqrt{k},
    \sqrt{k}) where k = \frac{groups}{C_\text{out} *
    \prod_{i=0}^{2}\text{kernel\_size}[i]}

Examples:
  >>> # With square kernels and equal stride
  >>> m = nn.ConvTranspose3d(16, 33, 3, stride=2)
  >>> # non-square kernels and unequal stride and with padding
  >>> m = nn.ConvTranspose3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(0, 4, 2))
  >>> input = torch.randn(20, 16, 10, 50, 100)
  >>> output = m(input)
