Implement aten_binear

[justinchuby]

Implement the aten_binear function at

onnxscript/onnxscript/function_libs/torch_lib/ops/core.py

Line 1192 in e67eeef

def aten_bilinear(

Operator schema: bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias=None) -> Tensor

Docs:

Applies a bilinear transformation to the incoming data: :math:`y = x_1^T A x_2 + b`.

    Args:
        in1_features: size of each first input sample, must be > 0
        in2_features: size of each second input sample, must be > 0
        out_features: size of each output sample, must be > 0
        bias: If set to ``False``, the layer will not learn an additive bias.
            Default: ``True``

    Shape:
        - Input1: :math:`(*, H_\text{in1})` where :math:`H_\text{in1}=\text{in1\_features}` and
          :math:`*` means any number of additional dimensions including none. All but the last dimension
          of the inputs should be the same.
        - Input2: :math:`(*, H_\text{in2})` where :math:`H_\text{in2}=\text{in2\_features}`.
        - Output: :math:`(*, H_\text{out})` where :math:`H_\text{out}=\text{out\_features}`
          and all but the last dimension are the same shape as the input.

    Attributes:
        weight: the learnable weights of the module of shape
            :math:`(\text{out\_features}, \text{in1\_features}, \text{in2\_features})`.
            The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
            :math:`k = \frac{1}{\text{in1\_features}}`
        bias:   the learnable bias of the module of shape :math:`(\text{out\_features})`.
                If :attr:`bias` is ``True``, the values are initialized from
                :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
                :math:`k = \frac{1}{\text{in1\_features}}`