Bilinear
class torch.nn.Bilinear(in1_features, in2_features, out_features, bias=True, device=None, dtype=None)
Applies a bilinear transformation to the incoming data: y = x_1^T A
   x_2 + b
Parameters:
      * in1_features (int) -- size of each first input sample
  * **in2_features** (*int*) -- size of each second 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:
      * Input1: (*, H_{in1}) where H_{in1}=\text{in1_features} and *
        means any number of additional dimensions including none. All
        but the last dimension of the inputs should be the same.
  * Input2: (*, H_{in2}) where H_{in2}=\text{in2\_features}.

  * Output: (*, H_{out}) where H_{out}=\text{out\_features} and
    all but the last dimension are the same shape as the input.

Variables: