class_docstr: `FeatureConnector` for generic data of arbitrary shape and type.
code: def decode_example_np(
      self, example_data: type_utils.NpArrayOrScalar
  ) -> type_utils.NpArrayOrScalar:
      example_data, shape = self._get_value_and_shape(example_data)
      if not self._encoded_to_bytes:
          if isinstance(example_data, np.ndarray) and shape:
              return example_data.reshape(shape)
          return example_data
      if self._encoding == Encoding.ZLIB:
          example_data = _execute_function_on_array_or_scalar(
              zlib.decompress, example_data
          )
      return _execute_function_on_array_or_scalar(
          _bytes_to_np_array,
          example_data,
          dtype=self._dtype,
          shape=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 7 for calling the Tensor obj's underlying member method code with example usage: obj.decode_example_np(example_data) and returns: _execute_function_on_array_or_scalar, example_data, example_data