class_docstr: Mock policy takes actions 1 and 2, alternating.
class_docstr_tok: ["Mock","policy","takes","actions","1","and","2",",","alternating","."]
code: def __init__(
      self,
      time_step_spec,
      action_spec,
      initial_policy_state=np.int32(2),
  ):
      policy_state_spec = specs.BoundedArraySpec(
          (), np.int32, minimum=1, maximum=2, name="policy_state_spec"
      )
      policy_info_spec = specs.BoundedArraySpec(
          (), np.int32, minimum=1, maximum=2, name="policy_info_spec"
      )
      self._initial_policy_state = initial_policy_state
      super(PyPolicyMock, self).__init__(
          time_step_spec,
          action_spec,
          policy_state_spec,
          policy_info_spec,
      )
      self.get_initial_state_call_count = 0
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any
  import numpy
  import tensorflow
  from tf_agents import specs
  from tf_agents.environments import py_environment
  from tf_agents.policies import py_policy
  from tf_agents.policies import tf_policy
  from tf_agents.replay_buffers import tf_uniform_replay_buffer
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
label_desc: Use image node_id 1 to create a new PyPolicyMock object from inherited base classes: py_policy with example: obj = PyPolicyMock(time_step_spec, action_spec, initial_policy_state)