class_docstr: A set of tests for the Learner.
class_docstr_tok: ["A","set","of","tests","for","the","Learner","."]
code: def testLearnerRun(self):
      strategy = tf.distribute.OneDeviceStrategy("/cpu:0")
  
      (
          test_learner,
          _,
          variables,
          _,
          _,
      ) = self._build_learner_with_strategy(
          dist_test_utils.create_dqn_agent_and_dataset_fn,
          strategy,
          sample_batch_size=4,
      )
      old_vars = self.evaluate(variables)
      loss = test_learner.run().loss
      new_vars = self.evaluate(variables)
  
      dist_test_utils.check_variables_different(
          self, old_vars, new_vars
      )
      self.assertAllInRange(loss, tf.float32.min, tf.float32.max)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import os
  from absl import logging
  from absl.testing import parameterized
  import numpy
  from six.moves import range
  import tensorflow
  from tf_agents.agents.behavioral_cloning import behavioral_cloning_agent
  from tf_agents.environments import suite_gym
  from tf_agents.environments import tf_py_environment
  from tf_agents.keras_layers import inner_reshape
  from tf_agents.networks import sequential
  from tf_agents.specs import tensor_spec
  from tf_agents.system import system_multiprocessing
  from tf_agents.train import learner
  from tf_agents.train.utils import test_utils
  from tf_agents.train.utils import train_utils
  from tf_agents.trajectories import time_step
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 4 for calling the LearnerTest obj's underlying member method code with example usage: obj.testLearnerRun() without return types