code: def get_state_spec(
      layer: tf.keras.layers.Layer,
  ) -> types.NestedTensorSpec:
      """Extracts the state spec from a layer.
  
      Args:
        layer: The layer to extract from; can be a `Network`.
  
      Returns:
        The state spec.
  
      Raises:
        TypeError: If `layer` is a subclass of `tf.keras.layers.RNN` (it must
          be wrapped by an `RNNWrapper` object).
        ValueError: If `layer` is a Keras layer and `create_variables` has
          not been called on it.
      """
      if isinstance(layer, Network):
          return layer.state_spec
  
      if isinstance(layer, tf.keras.layers.RNN):
          raise TypeError(
              "RNN Layer must be wrapped inside "
              "`tf_agents.keras_layers.RNNWrapper`: {}".format(layer)
          )
  
      initial_state = getattr(layer, "get_initial_state", None)
      state_size = getattr(layer, "state_size", None)
      if initial_state is not None and state_size is None:
          raise ValueError(
              "Layer lacks a `state_size` property.  Unable to extract state "
              "spec: {}".format(layer)
          )
      state_spec = ()
      if state_size is not None:
          state_spec = tf.nest.map_structure(
              lambda s: tf.TensorSpec(dtype=layer.dtype, shape=s),
              state_size,
          )
  
      return state_spec
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import typing
  import six
  import tensorflow
  import tensorflow_probability
  from tf_agents.distributions import utils
  from tf_agents.networks import layer_utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
  from tf_agents.utils import object_identity
  from tensorflow.python.trackable import base
  from tensorflow.python.util import tf_decorator
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 6 for calling a global function with example usage: get_state_spec(layer) and returns: state_spec, layer