
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{in} *
        \text{kernel_size}}

Examples:
  >>> m = nn.Conv1d(16, 33, 3, stride=2)
  >>> input = torch.randn(20, 16, 50)
  >>> output = m(input)
