def METHOD_NAME(self, model_name, io_names, reversed_order=False):
    triton_client = httpclient.InferenceServerClient(
        "localhost:8000", verbose=False
    )
    # Create the data for the two inputs. Initialize the first to unique
    # integers and the second to all ones.
    input0_data = np.arange(start=0, stop=16, dtype=np.float32)
    input0_data = np.expand_dims(input0_data, axis=0)
    input1_data = np.full(shape=(1, 16), fill_value=-1, dtype=np.float32)
    inputs = []
    output_req = []
    inputs.append(
        httpclient.InferInput(
            io_names[0] if not reversed_order else io_names[1], [1, 16], "FP32"
        )
    )
    inputs[-1].set_data_from_numpy(input0_data)
    inputs.append(
        httpclient.InferInput(
            io_names[1] if not reversed_order else io_names[0], [1, 16], "FP32"
        )
    )
    inputs[-1].set_data_from_numpy(input1_data)
    output_req.append(
        httpclient.InferRequestedOutput(io_names[2], binary_data=True)
    )
    output_req.append(
        httpclient.InferRequestedOutput(io_names[3], binary_data=True)
    )
    results = triton_client.infer(model_name, inputs, outputs=output_req)
    output0_data = results.as_numpy(
        io_names[2] if not reversed_order else io_names[3]
    )
    output1_data = results.as_numpy(
        io_names[3] if not reversed_order else io_names[2]
    )
    for i in range(16):
        self.assertEqual(input0_data[0][i] - input1_data[0][i], output0_data[0][i])
        self.assertEqual(input0_data[0][i] + input1_data[0][i], output1_data[0][i])