code: def evaluate_model(
      classifier,
      validate_tf_file,
      tfma_eval_result_path,
      selected_slice,
      label,
      feature_map,
  ):
      """Evaluate Model using Tensorflow Model Analysis.
  
      Args:
        classifier: Trained classifier model to be evaluted.
        validate_tf_file: File containing validation TFRecordDataset.
        tfma_eval_result_path: Directory path where eval results will be written.
        selected_slice: Feature for slicing the data.
        label: Groundtruth label.
        feature_map: Dict of feature names to their data type.
      """
  
      def eval_input_receiver_fn():
          """Eval Input Receiver function."""
          serialized_tf_example = tf.compat.v1.placeholder(
              dtype=tf.string,
              shape=[None],
              name="input_example_placeholder",
          )
  
          receiver_tensors = {"examples": serialized_tf_example}
  
          features = tf.io.parse_example(
              serialized_tf_example, feature_map
          )
          features["weight"] = tf.ones_like(features[label])
  
          return tfma.export.EvalInputReceiver(
              features=features,
              receiver_tensors=receiver_tensors,
              labels=features[label],
          )
  
      tfma_export_dir = tfma.export.export_eval_savedmodel(
          estimator=classifier,
          export_dir_base=os.path.join(
              tempfile.gettempdir(), "tfma_eval_model"
          ),
          eval_input_receiver_fn=eval_input_receiver_fn,
      )
  
      # Define slices that you want the evaluation to run on.
      slice_spec = [
          tfma.slicer.SingleSliceSpec(),  # Overall slice
          tfma.slicer.SingleSliceSpec(columns=[selected_slice]),
      ]
  
      # Add the fairness metrics.
      # pytype: disable=module-attr
      add_metrics_callbacks = [
          tfma.post_export_metrics.fairness_indicators(
              thresholds=[0.1, 0.3, 0.5, 0.7, 0.9], labels_key=label
          )
      ]
      # pytype: enable=module-attr
  
      eval_shared_model = tfma.default_eval_shared_model(
          eval_saved_model_path=tfma_export_dir,
          add_metrics_callbacks=add_metrics_callbacks,
      )
  
      # Run the fairness evaluation.
      tfma.run_model_analysis(
          eval_shared_model=eval_shared_model,
          data_location=validate_tf_file,
          output_path=tfma_eval_result_path,
          slice_spec=slice_spec,
      )
  
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 2 for calling a global function with example usage: evaluate_model(classifier, validate_tf_file, tfma_eval_result_path, selected_slice, label, feature_map) and returns: tfma