code: def timing_metrics(
      *,
      minibatch=False,
      epoch=False,
      epoch_running=False,
      experience=False,
      stream=False,
  ) -> List[TimePluginMetric]:
      """
      Helper method that can be used to obtain the desired set of
      plugin metrics.
  
      :param minibatch: If True, will return a metric able to log the train
          minibatch elapsed time.
      :param epoch: If True, will return a metric able to log the train epoch
          elapsed time.
      :param epoch_running: If True, will return a metric able to log the running
          train epoch elapsed time.
      :param experience: If True, will return a metric able to log the eval
          experience elapsed time.
      :param stream: If True, will return a metric able to log the eval stream
          elapsed time.
  
      :return: A list of plugin metrics.
      """
  
      metrics: List[TimePluginMetric] = []
      if minibatch:
          metrics.append(MinibatchTime())
  
      if epoch:
          metrics.append(EpochTime())
  
      if epoch_running:
          metrics.append(RunningEpochTime())
  
      if experience:
          metrics.append(ExperienceTime())
  
      if stream:
          metrics.append(StreamTime())
  
      return metrics
  
imports: import time
  from typing import TYPE_CHECKING, List
  from avalanche.evaluation import Metric, PluginMetric, GenericPluginMetric
  from avalanche.evaluation.metric_results import MetricValue, MetricResult
  from avalanche.evaluation.metric_utils import get_metric_name
  from avalanche.evaluation.metrics.mean import Mean
label_desc: Use image node_id 1 for calling a global function with example usage: timing_metrics() and returns: metrics