\text{padding}[0] - \text{dilation}[0]           \times
           (\text{kernel_size}[0] - 1) - 1}{\text{stride}[0]} +
           1\right\rfloor
       W_{out} = \left\lfloor\frac{W_{in}  + 2 \times
       \text{padding}[1] - \text{dilation}[1]           \times
       (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} +
       1\right\rfloor

Variables:
      * weight (Tensor) -- the learnable weights of the module
        of shape (\text{out_channels},
        \frac{\text{in_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{in} *
        \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},
