code: def features_encode_decode_np(
      serialize_fdict, deserialize_fdict, example, decoders
  ):
      """Runs the full pipeline: encode > write > tmp files > read > decode."""
      # Serialize/deserialize the example using NumPy methods.
      serialized_example = serialize_fdict.serialize_example(example)
      deserialized_example = deserialize_fdict.deserialize_example_np(
          serialized_example, decoders=decoders
      )
      return deserialized_example
  
imports: import contextlib
  import dataclasses
  import functools
  from typing import Any, Optional, Type
  import dill
  from etils import enp
  import numpy
  from tensorflow_datasets.core import dataset_utils
  from tensorflow_datasets.core import features
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.data_sources import python
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.utils import tree_utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  from tensorflow_datasets.testing import test_case
  from tensorflow_datasets.testing import test_utils
label_desc: Use image node_id 2 for calling a global function with example usage: features_encode_decode_np(serialize_fdict, deserialize_fdict, example, decoders) and returns: deserialized_example