code: def _create_example_tfrecord(self):
      example = text_format.Parse(
          """
          features {
            feature { key: "comment_text"
                      value { bytes_list { value: [ "comment 1" ] }}
                    }
            feature { key: "toxicity" value { float_list { value: [ 0.1 ] }}}
  
            feature { key: "heterosexual" value { float_list { value: [ 0.1 ] }}}
            feature { key: "homosexual_gay_or_lesbian"
                      value { float_list { value: [ 0.1 ] }}
                    }
            feature { key: "bisexual" value { float_list { value: [ 0.5 ] }}}
            feature { key: "other_sexual_orientation"
                      value { float_list { value: [ 0.1 ] }}
                    }
  
            feature { key: "male" value { float_list { value: [ 0.1 ] }}}
            feature { key: "female" value { float_list { value: [ 0.2 ] }}}
            feature { key: "transgender" value { float_list { value: [ 0.3 ] }}}
            feature { key: "other_gender" value { float_list { value: [ 0.4 ] }}}
  
            feature { key: "christian" value { float_list { value: [ 0.0 ] }}}
            feature { key: "jewish" value { float_list { value: [ 0.1 ] }}}
            feature { key: "muslim" value { float_list { value: [ 0.2 ] }}}
            feature { key: "hindu" value { float_list { value: [ 0.3 ] }}}
            feature { key: "buddhist" value { float_list { value: [ 0.4 ] }}}
            feature { key: "atheist" value { float_list { value: [ 0.5 ] }}}
            feature { key: "other_religion"
                      value { float_list { value: [ 0.6 ] }}
                    }
  
            feature { key: "black" value { float_list { value: [ 0.1 ] }}}
            feature { key: "white" value { float_list { value: [ 0.2 ] }}}
            feature { key: "asian" value { float_list { value: [ 0.3 ] }}}
            feature { key: "latino" value { float_list { value: [ 0.4 ] }}}
            feature { key: "other_race_or_ethnicity"
                      value { float_list { value: [ 0.5 ] }}
                    }
  
            feature { key: "physical_disability"
                      value { float_list { value: [ 0.6 ] }}
                    }
            feature { key: "intellectual_or_learning_disability"
                      value { float_list { value: [ 0.7 ] }}
                    }
            feature { key: "psychiatric_or_mental_illness"
                      value { float_list { value: [ 0.8 ] }}
                    }
            feature { key: "other_disability"
                      value { float_list { value: [ 1.0 ] }}
                    }
          }
          """,
          tf.train.Example(),
      )
      empty_comment_example = text_format.Parse(
          """
          features {
            feature { key: "comment_text"
                      value { bytes_list {} }
                    }
            feature { key: "toxicity" value { float_list { value: [ 0.1 ] }}}
          }
          """,
          tf.train.Example(),
      )
      return [example, empty_comment_example]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import csv
  import os
  import tempfile
  from unittest import mock
  from fairness_indicators.tutorial_utils import util
  import pandas
  import tensorflow
  import tensorflow_model_analysis
  from google.protobuf import text_format
label_desc: Use image node_id 1 for calling the UtilTest obj's underlying member method code with example usage: obj._create_example_tfrecord() and returns: list