class_docstr: The Sequential Network represents a sequence of Keras layers.
  
  It is a TF-Agents network that should be used instead of
  tf.keras.layers.Sequential. In contrast to keras Sequential, this layer can be
  used as a pure Layer in tf.functions and when exporting SavedModels, without
  having to pre-declare input and output shapes. In turn, this layer is usable
  as a preprocessing layer for TF Agents Networks, and can be exported via
  PolicySaver.
  
  Stateful Keras layers (e.g. LSTMCell, RNN, LSTM, TF-Agents DynamicUnroll)
  are all supported.  The `state_spec` of `Sequential` is a tuple whose
  length matches the number of stateful layers passed.  If no stateful layers
  or networks are passed to `Sequential` then `state_spec == ()`. Given that
  the replay buffers do not support specs with lists due to tf.nest vs
  tf.data.nest conflicts `Sequential` will also guarantee that all specs do not
  contain lists.
  
  Usage:
  ```python
  c = Sequential([layer1, layer2, layer3])
  output, next_state = c(inputs, state)
  ```
code: def compute_output_signature(
      self, input_signature: types.NestedSpec
  ) -> types.NestedSpec:
      output_signature = input_signature
      for l in self._sequential_layers:
          output_signature = l.compute_output_signature(
              output_signature
          )
      return output_signature
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  from typing import Any, List, Mapping, Optional, Sequence, Text, Tuple, Union
  import tensorflow
  from tf_agents.keras_layers import rnn_wrapper
  from tf_agents.networks import network
  from tf_agents.typing import types
  from tf_agents.utils import common
label_desc: Use image node_id 6 for calling the Sequential obj's underlying member method code with example usage: obj.compute_output_signature(input_signature) and returns: output_signature