(\text{kernel_size}[0] - 1) + \text{output_padding}[0] + 1
     W_{out} = (W_{in} - 1) \times \text{stride}[1] - 2 \times
     \text{padding}[1] + \text{dilation}[1]           \times
     (\text{kernel\_size}[1] - 1) + \text{output\_padding}[1] + 1

Variables:
      * weight (Tensor) -- the learnable weights of the module
        of shape (\text{in_channels},
        \frac{\text{out_channels}}{\text{groups}},
        \text{kernel_size[0]}, \text{kernel_size[1]}). The values of
        these weights are sampled from \mathcal{U}(-\sqrt{k},
        \sqrt{k}) where k = \frac{groups}{C_\text{out} *
        \prod_{i=0}^{1}\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}^{1}\text{kernel\_size}[i]}

Examples: