class_docstr: Top-level `FeatureConnector` to manage decoding.
  
  Note that `FeatureConnector` which are declared as `TopLevelFeature` can be
  nested. However, only the top-level feature should be decoded.
  
  `TopLevelFeature` allows better control over the decoding, and
  eventually better support for augmentations.
class_docstr_tok: ["Top-level","`","FeatureConnector","`","to","manage","decoding",".","Note","that","`","FeatureConnector","`","which","are","declared","as","`","TopLevelFeature","`","can","be","nested",".","However",",","only","the","top-level","feature","should","be","decoded",".","`","TopLevelFeature","`","allows","better","control","over","the","decoding",",","and","eventually","better","support","for","augmentations","."]
code: def decode_example(self, serialized_example, *, decoders=None):
      return self._decode_example_generic(
          serialized_example, decoders=decoders, backend=_Backend.TF
      )
  
imports: from __future__ import annotations
  import enum
  import functools
  from typing import Any, List, Union
  from tensorflow_datasets.core import example_parser
  from tensorflow_datasets.core import example_serializer
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 5 for calling the TopLevelFeature obj's underlying member method code with example usage: obj.decode_example(serialized_example) and returns: self