def METHOD_NAME(self):
    try:
        triton_client = httpclient.InferenceServerClient(
            url=f"{_tritonserver_ipaddr}:8000"
        )
    except Exception as e:
        print("channel creation failed: " + str(e))
        sys.exit(1)
    model_name = "libtorch_int32_int32_int32"
    inputs = []
    outputs = []
    inputs.append(httpclient.InferInput("INPUT0", [1, 16], "INT32"))
    inputs.append(httpclient.InferInput("INPUT1", [1, 16], "INT32"))
    # Create the data for the two input tensors. Initialize the first
    # to unique integers and the second to all ones.
    input0_data = np.arange(start=0, stop=16, dtype=np.int32)
    input0_data = np.expand_dims(input0_data, axis=0)
    input1_data = np.full(shape=(1, 16), fill_value=-1, dtype=np.int32)
    # Initialize the data
    inputs[0].set_data_from_numpy(input0_data, binary_data=True)
    inputs[1].set_data_from_numpy(input1_data, binary_data=True)
    outputs.append(httpclient.InferRequestedOutput("OUTPUT__0", binary_data=True))
    outputs.append(httpclient.InferRequestedOutput("OUTPUT__1", binary_data=True))
    results = triton_client.infer(model_name, inputs, outputs=outputs)
    output0_data = results.as_numpy("OUTPUT__0")
    output1_data = results.as_numpy("OUTPUT__1")
    # Validate the results by comparing with precomputed values.
    for i in range(16):
        print(
            str(input0_data[0][i])
            + " - "
            + str(input1_data[0][i])
            + " = "
            + str(output0_data[0][i])
        )
        print(
            str(input0_data[0][i])
            + " + "
            + str(input1_data[0][i])
            + " = "
            + str(output1_data[0][i])
        )
        if (input0_data[0][i] - input1_data[0][i]) != output0_data[0][i]:
            print("sync infer error: incorrect difference")
            sys.exit(1)
        if (input0_data[0][i] + input1_data[0][i]) != output1_data[0][i]:
            print("sync infer error: incorrect sum")
            sys.exit(1)