class_docstr: `Aim` tracking client handler to log parameters and metrics during the training
  and validation.
  
  Args:
      repo: Aim repository path
      experiment: Aim run's experiment name
      train_metric_prefix: prefix to identify training metrics
      val_metric_prefix: prefix to identify validation metrics
      test_metric_prefix: prefix to identify test metrics
      system_tracking_interval: sets the tracking interval in seconds
          for system usage metrics (CPU, Memory, etc.)
      log_system_params: flag to enable system params logging, True by default
class_docstr_tok: ["`","Aim","`","tracking","client","handler","to","log","parameters","and","metrics","during","the","training","and","validation",".","Args",":","repo",":","Aim","repository","path","experiment",":","Aim","run","'s","experiment","name","train_metric_prefix",":","prefix","to","identify","training","metrics","val_metric_prefix",":","prefix","to","identify","validation","metrics","test_metric_prefix",":","prefix","to","identify","test","metrics","system_tracking_interval",":","sets","the","tracking","interval","in","seconds","for","system","usage","metrics","(","CPU",",","Memory",",","etc",".",")","log_system_params",":","flag","to","enable","system","params","logging",",","True","by","default"]
code: def _create_opt_params_handler(
      self, *args: Any, **kwargs: Any
  ) -> "OptimizerParamsHandler":
      return OptimizerParamsHandler(*args, **kwargs)
  
imports: from typing import Any, Callable, Dict, List, Optional, Union
  from aim import Run
  from aim.ext.resource.configs import DEFAULT_SYSTEM_TRACKING_INT
label_desc: Use image node_id 11 for calling the AimLogger obj's underlying member method code with example usage: obj._create_opt_params_handler() and returns: OptimizerParamsHandler