class_docstr: `FeatureConnector` for generic data of arbitrary shape and type.
code: def from_json_content(
      cls, value: Union[Json, feature_pb2.TensorFeature]
  ) -> "Tensor":
      if isinstance(value, dict):
          return cls(
              shape=tuple(value["shape"]),
              dtype=feature_lib.dtype_from_str(value["dtype"]),
              # Use .get for backward-compatibility
              encoding=value.get("encoding", Encoding.NONE),
          )
      return cls(
          shape=feature_lib.from_shape_proto(value.shape),
          dtype=feature_lib.dtype_from_str(value.dtype),
          encoding=value.encoding or Encoding.NONE,
      )
  
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 11 for calling the Tensor obj's underlying member method code with example usage: obj.from_json_content(cls, value) and returns: cls, cls