code: def testInfoSpec(self):
      PolicyInfo = (
          collections.namedtuple(  # pylint: disable=invalid-name
              "PolicyInfo",
              (
                  "log_probability",
                  "predicted_rewards",
                  "bandit_policy_type",
              ),
          )
      )
      # Set default empty tuple for all fields.
      PolicyInfo.__new__.__defaults__ = ((),) * len(PolicyInfo._fields)
  
      info_spec = PolicyInfo(
          bandit_policy_type=self._bandit_policy_type_spec,
          log_probability=tensor_spec.BoundedTensorSpec(
              shape=(),
              dtype=tf.float32,
              maximum=0,
              minimum=-float("inf"),
              name="log_probability",
          ),
      )
  
      policy_with_info_spec = fixed_policy.FixedPolicy(
          np.asarray(self._greedy_action, dtype=np.int32),
          self._time_step_spec,
          self._action_spec,
          policy_info=PolicyInfo(
              bandit_policy_type=self._bandit_policy_type
          ),
          info_spec=info_spec,
      )
  
      epsilon = 0.2
      policy = epsilon_greedy_policy.EpsilonGreedyPolicy(
          policy_with_info_spec, epsilon=epsilon
      )
      self.assertEqual(policy.time_step_spec, self._time_step_spec)
      self.assertEqual(policy.action_spec, self._action_spec)
  
      time_step = tf.nest.map_structure(
          tf.convert_to_tensor, self._time_step
      )
  
      @common.function
      def action_step_fn(time_step=time_step):
          return policy.action(time_step, policy_state=(), seed=54)
  
      tf.nest.assert_same_structure(
          self._action_spec,
          self.evaluate(action_step_fn(time_step)).action,
      )
  
      if tf.executing_eagerly():
          action_step = action_step_fn
      else:
          action_step = action_step_fn()
  
      step = self.evaluate(action_step)
      tf.nest.assert_same_structure(info_spec, step.info)
  
      self.checkBanditPolicyTypeShape(
          step.info.bandit_policy_type, batch_size=2
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import math
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.policies import epsilon_greedy_policy
  from tf_agents.policies import fixed_policy
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  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 5 for calling the EpsilonGreedyPolicyTest obj's underlying member method code with example usage: obj.testInfoSpec() and returns: policy