code: def features_encode_decode(
      serialize_fdict, deserialize_fdict, example, decoders
  ):
      """Runs the full pipeline: encode > write > tmp files > read > decode."""
      # Serialize/deserialize the example using TensorFlow methods.
      serialized_example = serialize_fdict.serialize_example(example)
  
      decode_fn = functools.partial(
          deserialize_fdict.deserialize_example,
          decoders=decoders,
      )
      ds = tf.data.Dataset.from_tensors(serialized_example)
      ds = ds.map(decode_fn)
  
      if tf.executing_eagerly():
          out_tensor = next(iter(ds))
      else:
          out_tensor = tf.compat.v1.data.make_one_shot_iterator(
              ds
          ).get_next()
      out_numpy = dataset_utils.as_numpy(out_tensor)
      return out_tensor, out_numpy, ds.element_spec
  
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 1 for calling a global function with example usage: features_encode_decode(serialize_fdict, deserialize_fdict, example, decoders) and returns: out_tensor,  out_numpy,  ds