code: def _dict_to_ragged(example_data, tensor_info):
      """Reconstruct the ragged tensor from the row ids."""
      return tf.RaggedTensor.from_nested_row_lengths(
          flat_values=example_data["ragged_flat_values"],
          nested_row_lengths=[
              example_data["ragged_row_lengths_{}".format(k)]
              for k in range(tensor_info.sequence_rank - 1)
          ],
      )
  
imports: from __future__ import annotations
  import abc
  from collections.abc import Iterable, Mapping, Sequence
  import dataclasses
  import re
  from typing import Any, Union
  import numpy
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.utils import dtype_utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  from tensorflow_datasets.proto import tf_example_pb2
  from tensorflow_datasets.proto import tf_feature_pb2
label_desc: Use image node_id 10 for calling a global function with example usage: _dict_to_ragged(example_data, tensor_info) and returns: tf