class_docstr: `FeatureConnector` for generic data of arbitrary shape and type.
code: def _get_value_and_shape(self, example_data):
      if self._dynamic_shape:
          value = example_data["value"]
          # Extract the shape (while using static values when available)
          if enp.lazy.has_tf and isinstance(
              example_data["shape"], tf.Tensor
          ):
              shape = utils.merge_shape(
                  example_data["shape"], self._shape
              )
          else:
              shape = utils.merge_shape(
                  example_data["shape"][: len(value)], self._shape
              )
      else:
          value = example_data
          shape = np_utils.to_np_shape(self._shape)
      return value, shape
  
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 5 for calling the Tensor obj's underlying member method code with example usage: obj._get_value_and_shape(example_data) and returns: value,  shape