class_docstr: Recurrent value network. Reduces to 1 value output per batch item.
class_docstr_tok: ["Recurrent","value","network",".","Reduces","to","1","value","output","per","batch","item","."]
code: def call(
      self,
      observation,
      step_type=None,
      network_state=(),
      training=False,
  ):
      state, network_state = self._lstm_encoder(
          observation,
          step_type=step_type,
          network_state=network_state,
          training=training,
      )
      value = self._postprocessing_layers(state, training=training)
      return tf.squeeze(value, -1), network_state
  
file_path: agents/tf_agents/networks/value_rnn_network.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gin
  import tensorflow
  from tf_agents.networks import lstm_encoding_network
  from tf_agents.networks import network
label_desc: Use image node_id 2 for calling the ValueRnnNetwork obj's underlying member method code with example usage: obj.call(observation, step_type, network_state, training) and returns: tf,  network_state