class_docstr: Light-weight class for holding mutated values in the example.
  
  Should not be created by hand but rather generated via `make_mutant_features`.
  
  Used to represent a "mutant example": an example that is mostly identical to
  the user-provided original example, but has one feature that is different.
  
  Attributes:
    original_feature: An `OriginalFeatureList` object representing the feature
      to create mutants for.
    index: The index of the feature to create mutants for. The feature can be
      a repeated field, and we want to plot mutations of its various indices.
    mutant_value: The proposed mutant value for the given index.
  
  Raises:
    ValueError: If MutantFeatureValue fails init validation.
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docstr: Inits MutantFeatureValue.
docstr_tok: ["Inits","MutantFeatureValue","."]
code: def __init__(self, original_feature, index, mutant_value):
      """Inits MutantFeatureValue."""
      if not isinstance(original_feature, OriginalFeatureList):
          raise ValueError(
              "original_feature should be `OriginalFeatureList`, but had "
              "unexpected type: {}".format(type(original_feature))
          )
      self.original_feature = original_feature
  
      if index is not None and not isinstance(index, integer_types):
          raise ValueError(
              "index should be None or int, but had unexpected type: {}".format(
                  type(index)
              )
          )
      self.index = index
      self.mutant_value = (
          mutant_value.encode()
          if isinstance(mutant_value, string_types)
          else mutant_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 MutantFeatureValue object from inherited base classes: object with example: obj = MutantFeatureValue(original_feature, index, mutant_value)