code: def train_model(
      model_dir,
      train_tf_file,
      label,
      text_feature,
      feature_map,
      module_spec="https://tfhub.dev/google/nnlm-en-dim128/1",
  ):
      """Train model using DNN Classifier.
  
      Args:
        model_dir: Directory path to save trained model.
        train_tf_file: File containing training TFRecordDataset.
        label: Groundtruth label.
        text_feature: Text feature to be evaluated.
        feature_map: Dict of feature names to their data type.
        module_spec: A module spec defining the module to instantiate or a path
          where to load a module spec.
  
      Returns:
        Trained DNNClassifier.
      """
  
      def train_input_fn():
          """Train Input function."""
  
          def parse_function(serialized):
              parsed_example = tf.io.parse_single_example(
                  serialized=serialized, features=feature_map
              )
              # Adds a weight column to deal with unbalanced classes.
              parsed_example["weight"] = tf.add(
                  parsed_example[label], 0.1
              )
              return (parsed_example, parsed_example[label])
  
          train_dataset = (
              tf.data.TFRecordDataset(filenames=[train_tf_file])
              .map(parse_function)
              .batch(512)
          )
          return train_dataset
  
      text_embedding_column = hub.text_embedding_column(
          key=text_feature, module_spec=module_spec
      )
  
      classifier = tf_estimator.DNNClassifier(
          hidden_units=[500, 100],
          weight_column="weight",
          feature_columns=[text_embedding_column],
          n_classes=2,
          optimizer=tf.train.AdagradOptimizer(learning_rate=0.003),
          model_dir=model_dir,
      )
  
      classifier.train(input_fn=train_input_fn, steps=1000)
      return classifier
  
file_path: fairness-indicators/fairness_indicators/example_model.py
imports: import os
  import tempfile
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_hub
  import tensorflow_model_analysis
  from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators
label_desc: Use image node_id 1 for calling a global function with example usage: train_model(model_dir, train_tf_file, label, text_feature, feature_map, module_spec) and returns: classifier, train_dataset, parsed_example,  parsed_example