code: def predict(inputs_list, problem, request_fn):
      """Encodes inputs, makes request to deployed TF model, and decodes outputs."""
      assert isinstance(inputs_list, list)
      fname = "inputs" if problem.has_inputs else "targets"
      input_encoder = problem.feature_info[fname].encoder
      input_ids_list = [
          _encode(inputs, input_encoder, add_eos=problem.has_inputs)
          for inputs in inputs_list
      ]
      examples = [
          _make_example(input_ids, problem, fname)
          for input_ids in input_ids_list
      ]
      predictions = request_fn(examples)
      output_decoder = problem.feature_info["targets"].encoder
      outputs = [
          (
              _decode(prediction["outputs"], output_decoder),
              prediction["scores"],
          )
          for prediction in predictions
      ]
      return outputs
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import base64
  import functools
  from googleapiclient import discovery
  import grpc
  import numpy
  from tensor2tensor import problems
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.utils import cloud_mlengine
  from tensor2tensor.utils import contrib
  import tensorflow.compat.v1
  from tensorflow_serving.apis import predict_pb2
  from tensorflow_serving.apis import prediction_service_pb2_grpc
label_desc: Use image node_id 7 for calling a global function with example usage: predict(inputs_list, problem, request_fn) and returns: outputs