code: def make_mutant_features(
      original_feature, index_to_mutate, viz_params
  ):
      """Return a list of `MutantFeatureValue`s that are variants of original."""
      lower = viz_params.x_min
      upper = viz_params.x_max
      examples = viz_params.examples
      num_mutants = viz_params.num_mutants
  
      if original_feature.feature_type == "float_list":
          return [
              MutantFeatureValue(
                  original_feature, index_to_mutate, value
              )
              for value in np.linspace(lower, upper, num_mutants)
          ]
      elif original_feature.feature_type == "int64_list":
          mutant_values = (
              np.linspace(int(lower), int(upper), num_mutants)
              .astype(int)
              .tolist()
          )
          # Remove duplicates that can occur due to integer constraint.
          mutant_values = sorted(set(mutant_values))
          return [
              MutantFeatureValue(
                  original_feature, index_to_mutate, value
              )
              for value in mutant_values
          ]
      elif original_feature.feature_type == "bytes_list":
          feature_to_samples = get_categorical_features_to_sampling(
              examples, num_mutants
          )
  
          # `mutant_values` looks like:
          # [['Married-civ-spouse'], ['Never-married'], ['Divorced'], ['Separated']]
          mutant_values = feature_to_samples[
              original_feature.feature_name
          ]["samples"]
          return [
              MutantFeatureValue(original_feature, None, value)
              for value in mutant_values
          ]
      else:
          raise ValueError(
              "Malformed original feature had type of: "
              + original_feature.feature_type
          )
  
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 9 for calling a global function with example usage: make_mutant_features(original_feature, index_to_mutate, viz_params) and returns: unknown, unknown, unknown