class_docstr: Exposes a Python policy as an in-graph TensorFlow policy.
  
  # TODO(kbanoop): This class does not seem to handle batching/unbatching when
  # converting between TF and Py policies.
class_docstr_tok: ["Exposes","a","Python","policy","as","an","in-graph","TensorFlow","policy",".","#","TODO","(","kbanoop",")",":","This","class","does","not","seem","to","handle","batching\/unbatching","when","#","converting","between","TF","and","Py","policies","."]
docstr: Returns default [] representing a policy that has no variables.
docstr_tok: ["Returns","default","[","]","representing","a","policy","that","has","no","variables","."]
code: def _variables(self):
      """Returns default [] representing a policy that has no variables."""
      return []
  
file_path: agents/tf_agents/policies/tf_py_policy.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional, Text
  import tensorflow
  from tf_agents.policies import py_policy
  from tf_agents.policies import tf_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 4 for calling the TFPyPolicy obj's underlying member method code with example usage: obj._variables() and returns: list