def make_node(self, alpha, x, y, z):
        if not _is_sparse_variable(x) and not _is_sparse_variable(y):
            # If x and y are tensor, we don't want to use this class
            # We should use Dot22 and Gemm in that case.
            raise TypeError(x)

        dtype_out = scalar.upcast(alpha.type.dtype, x.type.dtype,
                                  y.type.dtype, z.type.dtype)
        alpha = tensor.as_tensor_variable(alpha)
        z = tensor.as_tensor_variable(z)

        assert z.ndim == 2
        assert alpha.type.broadcastable is (True,) * alpha.ndim
        if not _is_sparse_variable(x):
            x = tensor.as_tensor_variable(x)
            assert y.format in ["csr", "csc"]
            assert x.ndim == 2
        if not _is_sparse_variable(y):
            y = tensor.as_tensor_variable(y)
            assert x.format in ["csr", "csc"]
            assert y.ndim == 2

        return gof.Apply(self, [alpha, x, y, z],
                         [tensor.tensor(dtype=dtype_out,
                                        broadcastable=(False, False))])
dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/sparse/basic.py/Usmm.make_node/operator `==` -> `is`