The values are initialized from \mathcal{U}(-\sqrt{k},
        \sqrt{k}), where k = \frac{1}{\text{in_features}}
  * **bias** -- the learnable bias of the module of shape
    (\text{out\_features}). If "bias" is "True", the values are
    initialized from \mathcal{U}(-\sqrt{k}, \sqrt{k}) where k =
    \frac{1}{\text{in\_features}}

Examples:
  >>> m = nn.Linear(20, 30)
  >>> input = torch.randn(128, 20)
  >>> output = m(input)
  >>> print(output.size())
  torch.Size([128, 30])
