code: def setUp(self):
      super(MetricEqualityTest, self).setUp()
      observation_spec = array_spec.BoundedArraySpec(
          (1,), dtype=np.float32, minimum=0, maximum=10
      )
      self._action_spec = array_spec.BoundedArraySpec(
          (1,), dtype=np.float32, minimum=0, maximum=10
      )
      reward_spec = array_spec.BoundedArraySpec(
          (), dtype=np.float32, minimum=0, maximum=10
      )
      time_step_spec = ts.time_step_spec(observation_spec)
      self._time_step_spec = time_step_spec._replace(reward=reward_spec)
  
      self._tensor_action_spec = tensor_spec.from_spec(
          self._action_spec
      )
      self._tensor_time_step_spec = tensor_spec.from_spec(
          self._time_step_spec
      )
  
      self._env = random_py_environment.RandomPyEnvironment(
          observation_spec, self._action_spec
      )
      self._tf_env = tf_py_environment.TFPyEnvironment(self._env)
      self._policy = random_tf_policy.RandomTFPolicy(
          self._tensor_time_step_spec, self._tensor_action_spec
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from six.moves import range
  from six.moves import zip
  import tensorflow
  from tf_agents.drivers import dynamic_step_driver
  from tf_agents.environments import batched_py_environment
  from tf_agents.environments import random_py_environment
  from tf_agents.environments import tf_py_environment
  from tf_agents.metrics import py_metrics
  from tf_agents.metrics import tf_metrics
  from tf_agents.metrics import tf_py_metric
  from tf_agents.policies import random_tf_policy
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling the MetricEqualityTest obj's underlying member method code with example usage: obj.setUp() without return types