class_docstr: Composite `FeatureConnector` for a `dict` where each value is a list.
  
  `Sequence` correspond to sequence of `tfds.features.FeatureConnector`. At
  generation time, a list for each of the sequence element is given. The output
  of `tf.data.Dataset` will batch all the elements of the sequence together.
  
  If the length of the sequence is static and known in advance, it should be
  specified in the constructor using the `length` param.
  
  Note that `Sequence` does not support features which are of type
  `tf.io.FixedLenSequenceFeature`.
  
  Example:
  At construction time:
  
  ```
  tfds.features.Sequence(tfds.features.Image(), length=NB_FRAME)
  ```
  
  or:
  
  ```
  tfds.features.Sequence({
      'frame': tfds.features.Image(shape=(64, 64, 3))
      'action': tfds.features.ClassLabel(['up', 'down', 'left', 'right'])
  }, length=NB_FRAME)
  ```
  
  During data generation:
  
  ```
  yield {
      'frame': np.ones(shape=(NB_FRAME, 64, 64, 3)),
      'action': ['left', 'left', 'up', ...],
  }
  ```
  
  Tensor returned by `.as_dataset()`:
  
  ```
  {
      'frame': tf.Tensor(shape=(NB_FRAME, 64, 64, 3), dtype=tf.uint8),
      'action': tf.Tensor(shape=(NB_FRAME,), dtype=tf.int64),
  }
  ```
  
  At generation time, you can specify a list of features dict, a dict of list
  values or a stacked numpy array. The lists will automatically be distributed
  into their corresponding `FeatureConnector`.
docstr: See base class for details.
docstr_tok: ["See","base","class","for","details","."]
code: def _flatten(self, x):
      """See base class for details."""
      if isinstance(x, Sequence):
          return self.feature._flatten(
              x.feature
          )  # pylint: disable=protected-access
      return self.feature._flatten(x)
  
imports: from __future__ import annotations
  from typing import List, Optional, Union
  import numpy
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.features import feature
  from tensorflow_datasets.core.features import features_dict
  from tensorflow_datasets.core.features import tensor_feature
  from tensorflow_datasets.core.features import top_level_feature
  from tensorflow_datasets.core.proto import feature_pb2
  from tensorflow_datasets.core.utils import py_utils
  from tensorflow_datasets.core.utils import tree_utils
  from tensorflow_datasets.core.utils import type_utils
label_desc: Use image node_id 7 for calling the Sequence obj's underlying member method code with example usage: obj._flatten(x) and returns: self, self