def test_weight_transform_conv_identity(self, conv_type):
        """
        Test the weight transform function with an identity batchnorm layer.
        """
        # parameters for conv
        is_deconv = conv_type == "conv_transpose"
        conv_weight = np.arange(20).astype(np.float32)
        conv_weight = (
            np.reshape(conv_weight, (10, 2, 1, 1))
            if is_deconv
            else np.reshape(conv_weight, (20, 1, 1, 1))
        )
        conv_bias = np.arange(20).astype(np.float32)

        # parameters for batch_norm
        gamma = np.ones(20).astype(np.float32)
        beta = np.zeros(20).astype(np.float32)
        mean = np.zeros(20).astype(np.float32)
        variance = np.ones(20).astype(np.float32)
        epsilon = 0.0

        inputs = {
            "conv_weight": conv_weight,
            "conv_bias": conv_bias,
            "gamma": gamma,
            "beta": beta,
            "mean": mean,
            "variance": variance,
            "epsilon": epsilon,
        }

        new_conv_weight, new_conv_bias = self._apply_weight_transform(inputs, is_deconv)

        np.testing.assert_equal(new_conv_weight, conv_weight)
        np.testing.assert_equal(new_conv_bias, conv_bias)

        Test the weight transform function with an identity batchnorm layer.
        
coremltools/converters/mil/mil/passes/tests/test_passes.py