class_docstr: Sampler perf stats that will be included in rollout metrics.
code: def __init__(self, ema_coef: Optional[float] = None):
      # If not None, enable Exponential Moving Average mode.
      # The way we update stats is by:
      #     updated = (1 - ema_coef) * old + ema_coef * new
      # In general provides more responsive stats about sampler performance.
      # TODO(jungong) : make ema the default (only) mode if it works well.
      self.ema_coef = ema_coef
  
      self.iters = 0
      self.raw_obs_processing_time = 0.0
      self.inference_time = 0.0
      self.action_processing_time = 0.0
      self.env_wait_time = 0.0
      self.env_render_time = 0.0
  
imports: from collections import defaultdict
  import logging
  import time
  import tree
  from typing import TYPE_CHECKING, Dict, Iterator, List, Optional, Set, Tuple, Union
  import numpy
  from ray.rllib.env.base_env import ASYNC_RESET_RETURN, BaseEnv
  from ray.rllib.env.external_env import ExternalEnvWrapper
  from ray.rllib.env.wrappers.atari_wrappers import MonitorEnv, get_wrapper_by_cls
  from ray.rllib.evaluation.collectors.simple_list_collector import _PolicyCollectorGroup
  from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
  from ray.rllib.evaluation.episode_v2 import EpisodeV2
  from ray.rllib.evaluation.metrics import RolloutMetrics
  from ray.rllib.models.preprocessors import Preprocessor
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch, concat_samples
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.filter import Filter
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.spaces.space_utils import unbatch, get_original_space
  from ray.rllib.utils.typing import ActionConnectorDataType, AgentConnectorDataType, AgentID, EnvActionType, EnvID, EnvInfoDict, EnvObsType, MultiAgentDict, MultiEnvDict, PolicyID, PolicyOutputType, SampleBatchType, StateBatches, TensorStructType
  from ray.util.debug import log_once
label_desc: Use image node_id 1 to create a new _PerfStats object  with example: obj = _PerfStats(ema_coef)