code: def _make_example(input_ids, problem, input_feature_name="inputs"):
    
    features = {
        input_feature_name:
            tf.train.Feature(int64_list=tf.train.Int64List(value=input_ids))
    }
  
    # Fill in dummy values for any other required features that presumably
    # will not actually be used for prediction.
    data_fields, _ = problem.example_reading_spec()
    for fname, ftype in data_fields.items():
      if fname == input_feature_name:
        continue
      if not isinstance(ftype, tf.FixedLenFeature):
        # Only FixedLenFeatures are required
        continue
      if ftype.default_value is not None:
        # If there's a default value, no need to fill it in
        continue
      num_elements = functools.reduce(lambda acc, el: acc * el, ftype.shape, 1)
      if ftype.dtype in [tf.int32, tf.int64]:
        value = tf.train.Feature(
            int64_list=tf.train.Int64List(value=[0] * num_elements))
      if ftype.dtype in [tf.float32, tf.float64]:
        value = tf.train.Feature(
            float_list=tf.train.FloatList(value=[0.] * num_elements))
      if ftype.dtype == tf.bytes:
        value = tf.train.Feature(
            bytes_list=tf.train.BytesList(value=[""] * num_elements))
      tf.logging.info("Adding dummy value for feature %s as it is required by "
                      "the Problem.", fname)
      features[fname] = value
    return tf.train.Example(features=tf.train.Features(feature=features))
docs: Make a tf.train.Example for the problem.
  
    features[input_feature_name] = input_ids
  
    Also fills in any other required features with dummy values.
  
    Args:
      input_ids: list<int>.
      problem: Problem.
      input_feature_name: name of feature for input_ids.
  
    Returns:
      tf.train.Example