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_example(self, tfexample_data):
      """See base class for details."""
      value, shape = self._get_value_and_shape(tfexample_data)
      if self._encoded_to_bytes:
          if self._encoding == Encoding.ZLIB:
              value = tf.io.decode_compressed(
                  value, compression_type="ZLIB"
              )
          value = tf.io.decode_raw(value, self.tf_dtype)
          value = tf.reshape(value, shape)
  
      return value
  
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 6 for calling the Tensor obj's underlying member method code with example usage: obj.decode_example(tfexample_data) and returns: value