def METHOD_NAME(batch_size=1, num_iteration=20, warm_up_iteration=10):
    channel = grpc.insecure_channel(SERVER_URL)
    stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'transformer_lt_official'
    request.model_spec.signature_name = 'serving_default'
    batches, sorted_keys = input_generator_ts(DATA_FILE, VOCAB_FILE)
    translations = []
    batch = []
    inference_time = 0.0
    sentences_to_translate = min(batch_size * num_iteration, len(batches))
    sentences_after_warmup = 0
    for i, line in enumerate(batches[0:sentences_to_translate]):
        batch.append(line)
        if (i + 1) % batch_size == 0 or i == sentences_to_translate - 1:
            batch_num = (i // batch_size) + 1
            request.inputs['input'].CopyFrom(
                tf.make_tensor_proto(pd.DataFrame(batch).fillna(0).values.astype(np.int64)))
            start_time = time.time()
            result = stub.Predict(request)
            duration = time.time() - start_time
            shape = [int(dim.size) for dim in result.outputs['output'].tensor_shape.dim]
            translations += np.reshape(result.outputs['output'].int_val, shape).tolist()
            print('Iteration %d: %.3f sec' % (batch_num, duration))
            if batch_num > warm_up_iteration:
                inference_time += duration
                sentences_after_warmup += len(batch)
            batch = []
    average_time = inference_time / sentences_after_warmup
    print('Inferencing time: %s' % (inference_time))
    print('Batch size = %d' % batch_size)
    if batch_size == 1:
        print('Latency: %.3f ms' % (average_time * 1000))
    print('Throughput: %.3f sentences/sec' % (sentences_after_warmup / inference_time))
    if OUT_FILE:
        print('Decoding and saving translations to {}...'.format(OUT_FILE))
        decoded_translations = []
        for i, tr in enumerate(translations):
            decoded_translations.append(_trim_and_decode(tr, VOCAB_FILE))
        with tf.io.gfile.GFile(OUT_FILE, "w") as f:
            for i in sorted_keys:
                if i < len(decoded_translations):
                    f.write("%s\n" % decoded_translations[i])
        print('Done!')