class_docstr: `FeatureConnector` for generic data of arbitrary shape and type.
docstr: See base class for details.
docstr_tok: ["See","base","class","for","details","."]
code: def decode_batch_example(self, example_data):
      """See base class for details."""
      if self._dynamic_shape or self._encoded_to_bytes:
          # For Sequence(Tensor()), use `tf.map_fn` to decode/reshape individual
          # tensors.
          return super().decode_batch_example(example_data)
      else:
          # For regular tensors, `decode_example` is a no-op so can be applied
          # directly (avoid `tf.map_fn`)
          return self.decode_example(example_data)
  
imports: from __future__ import annotations
  import enum
  import functools
  from typing import Optional, Protocol, Tuple, TypeVar, Union
  import zlib
  from etils import enp
  import numpy
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.proto import feature_pb2
  from tensorflow_datasets.core.utils import dtype_utils
  from tensorflow_datasets.core.utils import np_utils
  from tensorflow_datasets.core.utils import py_utils
  from tensorflow_datasets.core.utils import type_utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 8 for calling the Tensor obj's underlying member method code with example usage: obj.decode_batch_example(example_data) and returns: super, self