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 __init__(self):
      info_spec = {
          "test": tensor_spec.BoundedTensorSpec([1], tf.int64, 0, 1)
      }
      super(TfDictInfoAndLogProbs, self).__init__(info_spec=info_spec)
  
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 1 to create a new TfDictInfoAndLogProbs object from inherited base classes: TfEmitLogProbsPolicy with example: obj = TfDictInfoAndLogProbs()