Linear
class torch.nn.Linear(in_features, out_features, bias=True, device=None, dtype=None)
Applies a linear transformation to the incoming data: y = xA^T + b
This module supports TensorFloat32.
On certain ROCm devices, when using float16 inputs this module will
   use different precision for backward.
Parameters:
      * in_features (int) -- size of each input sample
  * **out_features** (*int*) -- size of each output sample

  * **bias** (*bool*) -- If set to "False", the layer will not
    learn an additive bias. Default: "True"

Shape:
      * Input: (*, H_{in}) where * means any number of dimensions
        including none and H_{in} = \text{in_features}.
  * Output: (*, H_{out}) where all but the last dimension are the
    same shape as the input and H_{out} = \text{out\_features}.

Variables:
      * weight (torch.Tensor) -- the learnable weights of the
        module of shape (\text{out_features}, \text{in_features}).