class_docstr: Feed Forward network with CNN and FNN layers.
class_docstr_tok: ["Feed","Forward","network","with","CNN","and","FNN","layers","."]
code: def call(
      self,
      observation,
      step_type=None,
      network_state=(),
      training=False,
  ):
      del step_type  # unused.
  
      if self._batch_squash:
          outer_rank = nest_utils.get_outer_rank(
              observation, self.input_tensor_spec
          )
          batch_squash = utils.BatchSquash(outer_rank)
          observation = tf.nest.map_structure(
              batch_squash.flatten, observation
          )
  
      if self._flat_preprocessing_layers is None:
          processed = observation
      else:
          processed = []
          for obs, layer in zip(
              nest.flatten_up_to(self._preprocessing_nest, observation),
              self._flat_preprocessing_layers,
          ):
              processed.append(layer(obs, training=training))
          if (
              len(processed) == 1
              and self._preprocessing_combiner is None
          ):
              # If only one observation is passed and the preprocessing_combiner
              # is unspecified, use the preprocessed version of this observation.
              processed = processed[0]
  
      states = processed
  
      if self._preprocessing_combiner is not None:
          states = self._preprocessing_combiner(states)
  
      for layer in self._postprocessing_layers:
          states = layer(states, training=training)
  
      if self._batch_squash:
          states = tf.nest.map_structure(batch_squash.unflatten, states)
  
      return states, network_state
  
file_path: agents/tf_agents/networks/encoding_network.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import logging
  import gin
  from six.moves import zip
  import tensorflow
  from tf_agents.keras_layers import permanent_variable_rate_dropout
  from tf_agents.networks import network
  from tf_agents.networks import utils
  from tf_agents.utils import nest_utils
  from tensorflow.python.util import nest
label_desc: Use image node_id 2 for calling the EncodingNetwork obj's underlying member method code with example usage: obj.call(observation, step_type, network_state, training) and returns: states,  network_state