code: def init_metrics(self):
      metrics = self.opts["output"]["metrics"]
      for metric in metrics:
          meterClass = self.getMeterClass(metric["meter_py"])
          # log.info('metric.meter_kargs {}'.format(metric.meter_kargs))
          # log.info('type meter_kargs {}'.format(type(metric.meter_kargs)))
          meterInstance = meterClass(
              opts=self.opts, **metric["meter_kargs"]
          )
          self.add_metric(
              metric["name"], meterInstance, metric["is_train"]
          )
  
imports: from abc import abstractmethod
  from caffe2.python import workspace
  from caffe2.python import timeout_guard
  from caffe2.python import data_parallel_model
  from .None import checkpoint
  from .None import ModuleRegister
  from .None import module_map
  import os
  import inspect
  import time
  import logging
label_desc: Use image node_id 5 for calling the AnyExpTrainer obj's underlying member method code with example usage: obj.init_metrics() without return types