class_docstr: Dummy greedy policy for DummyEnvironment. Recommended action is constant w.r.t. the specified observation.
class_docstr_tok: ["Dummy","greedy","policy","for","DummyEnvironment",".","Recommended","action","is","constant","w.r.t",".","the","specified","observation","."]
docstr: Not implemented.
docstr_tok: ["Not","implemented","."]
code: def compute_top_action_candidates(
      self,
      observation: ObservationType,
      num_candidates: Optional[int],
      maze_state: Optional[MazeStateType],
      env: Optional[BaseEnv],
      actor_id: Optional[ActorID] = None,
  ) -> Tuple[Sequence[ActionType], Sequence[float]]:
      """
      Not implemented.
      """
      raise NotImplementedError
  
file_path: maze/maze/test/shared_test_utils/dummy_env/agents/dummy_policy.py
imports: from typing import Sequence, Tuple, Optional
  import numpy
  from maze.core.agent.policy import Policy
  from maze.core.annotations import override
  from maze.core.env.action_conversion import ActionType
  from maze.core.env.base_env import BaseEnv
  from maze.core.env.maze_state import MazeStateType
  from maze.core.env.observation_conversion import ObservationType
  from maze.core.env.structured_env import ActorID
label_desc: Use image node_id 3 for calling the DummyGreedyPolicy obj's underlying member method code with example usage: obj.compute_top_action_candidates(observation, num_candidates, maze_state, env, actor_id) without return types