class_docstr: Light-weight class for holding the original values in the example.
  
  Should not be created by hand, but rather generated via
  `parse_original_feature_from_example`. Just used to hold inferred info
  about the example.
  
  Attributes:
    feature_name: String name of the feature.
    original_value: The value of the feature in the original example.
    feature_type: One of ['int64_list', 'float_list'].
  
  Raises:
    ValueError: If OriginalFeatureList fails init validation.
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docstr: Inits OriginalFeatureList.
docstr_tok: ["Inits","OriginalFeatureList","."]
code: def __init__(self, feature_name, original_value, feature_type):
      """Inits OriginalFeatureList."""
      self.feature_name = feature_name
      self.original_value = [
          ensure_not_binary(value) for value in original_value
      ]
      self.feature_type = feature_type
  
      # Derived attributes.
      self.length = sum(1 for _ in original_value)
  
imports: from __future__ import division
  from __future__ import print_function
  import collections
  import copy
  import json
  import math
  from absl import logging
  import numpy
  import tensorflow
  from google.protobuf import json_format
  from six import binary_type, string_types, integer_types
  from six import iteritems
  from six.moves import zip
  from inspect import signature
  from utils import common_utils
  from utils import platform_utils
  from tensorflow_serving.apis import classification_pb2
  from tensorflow_serving.apis import inference_pb2
  from tensorflow_serving.apis import regression_pb2
label_desc: Use image node_id 1 to create a new OriginalFeatureList object from inherited base classes: object with example: obj = OriginalFeatureList(feature_name, original_value, feature_type)