def METHOD_NAME(
    self, client, model_name, input_name="INPUT0", output_name="OUTPUT0"
):
    # plan does not support large batch sizes.
    if not model_name.startswith("plan"):
        inputs = [
            client[0].InferInput(
                input_name,
                self._large_in0.shape,
                np_to_triton_dtype(self._data_type),
            )
        ]
        inputs[0].set_data_from_numpy(self._large_in0)
        results = client[1].infer(model_name, inputs)
        # if the inference is completed, examine results to ensure that
        # the framework and protocol do support large payload
        self.assertTrue(
            np.array_equal(self._large_in0, results.as_numpy(output_name)),
            "output is different from input",
        )
    if client[0] == httpclient:
        # FIXME HTTPServer cannot support large payloads. See DLIS-1776.
        inputs = [
            client[0].InferInput(
                input_name,
                self._very_large_in0.shape,
                np_to_triton_dtype(self._data_type),
            )
        ]
        inputs[0].set_data_from_numpy(self._very_large_in0)
        with self.assertRaises(InferenceServerException):
            results = client[1].infer(model_name, inputs)
    # FIXME Test is terminated due to libprotobuf FATAL error when GRPC sends
    # the second request with input tensors larger than 1.3GBs. In this test
    # GRPC has been currently exempted from testing for Very Large tensor(3GBs)
    # until the problem is resolved. Should be uncommented once the GRPC issue is resolved.
    # See DLIS-2474.
    # if client[0] == grpcclient:
    #     inputs = [
    #         client[0].InferInput(input_name, self._very_large_in0.shape,
    #                              np_to_triton_dtype(self._data_type))
    #     ]
    #     inputs[0].set_data_from_numpy(self._very_large_in0)
    #     # GRPC must fail for large payloads because of a 2GB protobuf limit
    #     with self.assertRaises(InferenceServerException):
    #         results = client[1].infer(model_name, inputs)
    # Send a small payload to verify if the server is still functional
    inputs = [
        client[0].InferInput(
            input_name, self._small_in0.shape, np_to_triton_dtype(self._data_type)
        )
    ]
    inputs[0].set_data_from_numpy(self._small_in0)
    results = client[1].infer(model_name, inputs)
    self.assertTrue(
        np.array_equal(self._small_in0, results.as_numpy(output_name)),
        "output is different from input",
    )