e488e3c4436b10caeed246573bdf030c65f434af,torch/nn/functional.py,,linear,#Any#Any#Any#,1737

Before Change

        return handle_torch_function(linear, (input, weight), input, weight, bias=bias)
    if input.dim() == 2 and bias is not None:
        // fused op is marginally faster
        ret = torch.addmm(bias, input, weight.t())
    else:
        output = input.matmul(weight.t())
        if bias is not None:
            output += bias
        ret = output
    return ret


def bilinear(input1: Tensor, input2: Tensor, weight: Tensor, bias: Optional[Tensor] = None) -> Tensor:
    r

After Change

    
    if has_torch_function_variadic(input, weight):
        return handle_torch_function(linear, (input, weight), input, weight, bias=bias)
    return torch._C._nn.linear(input, weight, bias)


def bilinear(input1: Tensor, input2: Tensor, weight: Tensor, bias: Optional[Tensor] = None) -> Tensor:
    r

Instances