code: def __init__(self, policy_map):
      self.policy_collectors = {}
      # Total env-steps (1 env-step=up to N agents stepped).
      self.env_steps = 0
      # Total agent steps (1 agent-step=1 individual agent (out of N)
      # stepped).
      self.agent_steps = 0
  
imports: import collections
  from gymnasium.spaces import Space
  import logging
  import numpy
  import tree
  from typing import Dict, List, Optional, Tuple, TYPE_CHECKING, Union
  from ray.rllib.env.base_env import _DUMMY_AGENT_ID
  from ray.rllib.evaluation.collectors.sample_collector import SampleCollector
  from ray.rllib.evaluation.collectors.agent_collector import AgentCollector
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_map import PolicyMap
  from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch, concat_samples
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.debug import summarize
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.spaces.space_utils import get_dummy_batch_for_space
  from ray.rllib.utils.typing import AgentID, EpisodeID, EnvID, PolicyID, TensorType, ViewRequirementsDict
  from ray.util.debug import log_once
label_desc: Use image node_id 1 to create a new _PolicyCollectorGroup object  with example: obj = _PolicyCollectorGroup(policy_map)