code: def _eval_input_receiver_using_iterator_fn():
      """Eval input receiver function using an iterator."""
      csv_row = tf.compat.v1.placeholder(
          dtype=tf.string, shape=[None], name="input_csv_row"
      )
      iterator = tf.compat.v1.data.make_initializable_iterator(
          tf.compat.v1.data.Dataset.from_tensors(csv_row)
      )
      features = _parse_csv(iterator.get_next())
      receiver_tensors = {"examples": csv_row}
  
      return export.EvalInputReceiver(
          features=features,
          labels=features["input_index"],
          receiver_tensors=receiver_tensors,
          input_refs=features["input_index"],
          iterator_initializer=iterator.initializer.name,
      )
  
file_path: model-analysis/tensorflow_model_analysis/eval_saved_model/example_trainers/fake_multi_examples_per_input_estimator.py
imports: import tensorflow
  from tensorflow import estimator
  from tensorflow_model_analysis.eval_saved_model import export
  from tensorflow_model_analysis.eval_saved_model.example_trainers import util
  from tensorflow.python.estimator.canned import metric_keys
label_desc: Use image node_id 4 for calling a global function with example usage: _eval_input_receiver_using_iterator_fn() and returns: export