code: def _decode_feature(
      feature,
      example,
      decoder,
      sequence_rank: int,
      backend: _Backend,
  ):
      """Decode a single feature."""
      if decoder is not None:
          # If the decoder is still a dict, it means that the feature is a Dataset
          # (it wasn't flattened).
          if isinstance(decoder, dict):
              decode_kwargs = dict(decoders=decoder)
              decoder = feature
          else:
              # Eventually overwrite the default decoding
              decode_kwargs = {}
              decoder.setup(feature=feature)
      else:
          decode_kwargs = {}
          decoder = feature
  
      if backend == _Backend.NP:
          if sequence_rank == 0:
              return decoder.decode_example_np(example, **decode_kwargs)
          elif sequence_rank == 1:
              return decoder.decode_batch_example_np(
                  example, **decode_kwargs
              )
          elif sequence_rank > 1:
              return decoder.decode_ragged_example_np(
                  example, **decode_kwargs
              )
      elif backend == _Backend.TF:
          if sequence_rank == 0:
              return decoder.decode_example(example, **decode_kwargs)
          elif sequence_rank == 1:
              # Return a batch of examples from a sequence
              return decoder.decode_batch_example(
                  example, **decode_kwargs
              )
          elif sequence_rank > 1:
              # Use ragged tensor if the sequance rank is greater than one
              return decoder.decode_ragged_example(
                  example, **decode_kwargs
              )
      else:
          raise ValueError(
              f"wrong backend {backend} to decode features."
          )
  
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 1 for calling a global function with example usage: _decode_feature(feature, example, decoder, sequence_rank, backend) and returns: decoder, decoder, decoder, decoder, decoder, decoder