code: def verifyVariableAssignAndRestore(
      self, observation_spec, action_spec, actor_net, loss_fn=None
  ):
      strategy = tf.distribute.get_strategy()
      time_step_spec = ts.time_step_spec(observation_spec)
      with strategy.scope():
          # Use BehaviorCloningAgent instead of AWRAgent to test the network.
          agent = behavioral_cloning_agent.BehavioralCloningAgent(
              time_step_spec,
              action_spec,
              cloning_network=actor_net,
              optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
              loss_fn=loss_fn,
          )
      # Assign all vars to 0.
      for var in tf.nest.flatten(agent.variables):
          var.assign(tf.zeros_like(var))
      # Save checkpoint
      ckpt_dir = self.create_tempdir()
      checkpointer = common.Checkpointer(ckpt_dir=ckpt_dir, agent=agent)
      global_step = tf.constant(0)
      checkpointer.save(global_step)
      # Assign all vars to 1.
      for var in tf.nest.flatten(agent.variables):
          var.assign(tf.ones_like(var))
      # Restore to 0.
      checkpointer._checkpoint.restore(
          checkpointer._manager.latest_checkpoint
      )
      for var in tf.nest.flatten(agent.variables):
          value = var.numpy()
          if isinstance(value, np.int64):
              self.assertEqual(value, 0)
          else:
              self.assertAllEqual(
                  value,
                  np.zeros_like(value),
                  msg="{} has var mean {}, expected 0.".format(
                      var.name, value
                  ),
              )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents import test_util
  from tf_agents.agents.behavioral_cloning import behavioral_cloning_agent
  from tf_agents.keras_layers import inner_reshape
  from tf_agents.networks import expand_dims_layer
  from tf_agents.networks import q_network
  from tf_agents.networks import q_rnn_network
  from tf_agents.networks import sequential
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 4 for calling the BehavioralCloningAgentTest obj's underlying member method code with example usage: obj.verifyVariableAssignAndRestore(observation_spec, action_spec, actor_net, loss_fn) without return types