class_docstr: A policy that chooses from a set of policies to decide the action.
class_docstr_tok: ["A","policy","that","chooses","from","a","set","of","policies","to","decide","the","action","."]
code: def _variables(self):
      variables = sum([p.variables() for p in self._policies], [])
      variables.extend(
          self._mixture_distribution.variables
      )  # pytype: disable=attribute-error  # dynamic-method-lookup
      return variables
  
file_path: agents/tf_agents/bandits/policies/mixture_policy.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional, Sequence, Text
  import gin
  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.typing import types
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 2 for calling the MixturePolicy obj's underlying member method code with example usage: obj._variables() and returns: variables