code: def _parse_csv(rows_string_tensor):
      """Takes the string input tensor and returns a dict of rank-2 tensors."""
      example_count = tf.io.decode_csv(
          records=rows_string_tensor,
          record_defaults=[
              tf.constant([0], dtype=tf.int32, shape=None)
          ],
      )[0]
  
      input_index, intra_input_index = _indices_from_example_count(
          example_count
      )
      annotation = tf.strings.join(
          [
              "raw_input: ",
              tf.gather(rows_string_tensor, input_index),
              "; index: ",
              tf.as_string(intra_input_index),
          ]
      )
  
      return {
          "example_count": tf.gather(example_count, input_index),
          "input_index": input_index,
          "intra_input_index": intra_input_index,
          "annotation": annotation,
      }
  
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 2 for calling a global function with example usage: _parse_csv(rows_string_tensor) and returns: dict