class_docstr: An implementation of the DQN agent.
class_docstr_tok: ["An","implementation","of","the","DQN","agent","."]
docstr: Builds the convolutional network used to compute the agent's Q-values.
  
  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","the","convolutional","network","used","to","compute","the","agent","'s","Q-values",".","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 the convolutional network used to compute the agent's Q-values.
  
      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, name=name)
      return network
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import os
  import random
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from dopamine.replay_memory import circular_replay_buffer
  import gin.tf
  import numpy
  import tensorflow
label_desc: Use image node_id 2 for calling the DQNAgent obj's underlying member method code with example usage: obj._create_network(name) and returns: network