class_docstr: Helper handler to log optimizer parameters
  
  Args:
      optimizer: torch optimizer or any object with attribute ``param_groups``
          as a sequence.
      param_name: parameter name
      tag: common title for all produced plots. For example, 'generator'
class_docstr_tok: ["Helper","handler","to","log","optimizer","parameters","Args",":","optimizer",":","torch","optimizer","or","any","object","with","attribute","``","param_groups","``","as","a","sequence",".","param_name",":","parameter","name","tag",":","common","title","for","all","produced","plots",".","For","example",",","'generator","'"]
code: def __init__(
      self,
      optimizer: Optimizer,
      param_name: str = "lr",
      tag: Optional[str] = None,
  ):
      super(OptimizerParamsHandler, self).__init__(
          optimizer, param_name, tag
      )
  
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 1 to create a new OptimizerParamsHandler object from inherited base classes: BaseOptimizerParamsHandler with example: obj = OptimizerParamsHandler(optimizer, param_name, tag)