class_docstr: The Minibatch Max RAM metric.
  
  This plugin metric only works at training time.
class_docstr_tok: ["The","Minibatch","Max","RAM","metric",".","This","plugin","metric","only","works","at","training","time","."]
docstr: Creates an instance of the Minibatch Max RAM metric.
  
  :param every: seconds after which update the maximum RAM
      usage
docstr_tok: ["Creates","an","instance","of","the","Minibatch","Max","RAM","metric",".",":","param","every",":","seconds","after","which","update","the","maximum","RAM","usage"]
code: def __init__(self, every=1):
      """Creates an instance of the Minibatch Max RAM metric.
  
      :param every: seconds after which update the maximum RAM
          usage
      """
      super(MinibatchMaxRAM, self).__init__(
          every, reset_at="iteration", emit_at="iteration", mode="train"
      )
  
imports: import os
  import time
  from typing import Optional, List, TYPE_CHECKING
  from threading import Thread
  from psutil import Process
  from avalanche.evaluation import Metric, PluginMetric, GenericPluginMetric
  from avalanche.evaluation.metric_results import MetricResult
label_desc: Use image node_id 1 to create a new MinibatchMaxRAM object from inherited base classes: RAMPluginMetric with example: obj = MinibatchMaxRAM(every)