class_docstr: A compact implementation of a simplified Rainbow agent.
class_docstr_tok: ["A","compact","implementation","of","a","simplified","Rainbow","agent","."]
docstr: Builds a convolutional network that outputs Q-value distributions.
  
  Args:
    name: str, this name is passed to the tf.keras.Model and used to create
      variable scope under the hood by the tf.keras.Model.
  Returns:
    network: tf.keras.Model, the network instantiated by the Keras model.
docstr_tok: ["Builds","a","convolutional","network","that","outputs","Q-value","distributions",".","Args",":","name",":","str",",","this","name","is","passed","to","the","tf.keras.Model","and","used","to","create","variable","scope","under","the","hood","by","the","tf.keras.Model",".","Returns",":","network",":","tf.keras.Model",",","the","network","instantiated","by","the","Keras","model","."]
code: def _create_network(self, name):
      """Builds a convolutional network that outputs Q-value distributions.
  
      Args:
        name: str, this name is passed to the tf.keras.Model and used to create
          variable scope under the hood by the tf.keras.Model.
      Returns:
        network: tf.keras.Model, the network instantiated by the Keras model.
      """
      network = self.network(
          self.num_actions, self._num_atoms, self._support, name=name
      )
      return network
  
file_path: dopamine/dopamine/agents/rainbow/rainbow_agent.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from dopamine.agents.dqn import dqn_agent
  from dopamine.discrete_domains import atari_lib
  from dopamine.replay_memory import prioritized_replay_buffer
  import gin.tf
  import tensorflow
label_desc: Use image node_id 2 for calling the RainbowAgent obj's underlying member method code with example usage: obj._create_network(name) and returns: network