code: def create_tensorrec_dataset_from_tfrecord(tfrecord_path):
      """
      Loads a TFRecord file and creates a Dataset with the contents.
      :param tfrecord_path: str
      :return: tf.data.Dataset
      """
  
      def parse_tensorrec_tfrecord(example_proto):
          features = {
              "row_index": tf.FixedLenSequenceFeature(
                  (), tf.int64, allow_missing=True
              ),
              "col_index": tf.FixedLenSequenceFeature(
                  (), tf.int64, allow_missing=True
              ),
              "values": tf.FixedLenSequenceFeature(
                  (), tf.float32, allow_missing=True
              ),
              "d0": tf.FixedLenFeature((), tf.int64),
              "d1": tf.FixedLenFeature((), tf.int64),
          }
          parsed_features = tf.parse_single_example(
              example_proto, features
          )
          return (
              parsed_features["row_index"],
              parsed_features["col_index"],
              parsed_features["values"],
              parsed_features["d0"],
              parsed_features["d1"],
          )
  
      dataset = tf.data.TFRecordDataset(tfrecord_path).map(
          parse_tensorrec_tfrecord
      )
      return dataset
  
file_path: tensorrec/tensorrec/input_utils.py
imports: import numpy
  from scipy import sparse
  import tensorflow
  from .session_management import get_session
label_desc: Use image node_id 6 for calling a global function with example usage: create_tensorrec_dataset_from_tfrecord(tfrecord_path) and returns: dataset, parsed_features,  parsed_features,  parsed_features,  parsed_features,  parsed_features