class_docstr: The Minibatch Max GPU metric.
  This plugin metric only works at training time.
class_docstr_tok: ["The","Minibatch","Max","GPU","metric",".","This","plugin","metric","only","works","at","training","time","."]
docstr: Creates an instance of the Minibatch Max GPU metric
  
  :param gpu_id: GPU device ID.
  :param every: seconds after which update the maximum GPU
      usage
docstr_tok: ["Creates","an","instance","of","the","Minibatch","Max","GPU","metric",":","param","gpu_id",":","GPU","device","ID",".",":","param","every",":","seconds","after","which","update","the","maximum","GPU","usage"]
code: def __init__(self, gpu_id, every=0.5):
      """
      Creates an instance of the Minibatch Max GPU metric
  
      :param gpu_id: GPU device ID.
      :param every: seconds after which update the maximum GPU
          usage
      """
      super(MinibatchMaxGPU, self).__init__(
          gpu_id,
          every,
          reset_at="iteration",
          emit_at="iteration",
          mode="train",
      )
  
imports: import GPUtil
  from threading import Thread
  import time
  import warnings
  from typing import Optional, TYPE_CHECKING, List
  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 MinibatchMaxGPU object from inherited base classes: GPUPluginMetric with example: obj = MinibatchMaxGPU(gpu_id, every)