class_docstr: Same dummy policy as above except it stores more things in info.
class_docstr_tok: ["Same","dummy","policy","as","above","except","it","stores","more","things","in","info","."]
code: def _distribution(self, time_step, policy_state):
      distribution_step = super(
          TfDictInfoAndLogProbs, self
      )._distribution(time_step=time_step, policy_state=policy_state)
      return distribution_step._replace(
          info={"test": tf.constant(1, dtype=tf.int64)}
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import cast
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.policies import tf_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  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 2 for calling the TfDictInfoAndLogProbs obj's underlying member method code with example usage: obj._distribution(time_step, policy_state) and returns: distribution_step