class_docstr: Base class for optimizers.
  
  This wraps a :class:`torch.optim.Optimizer` instance and provides support
  for parameter scheduling. Typical PyTorch optimizers are used like this:
  
      optim = SGD(model.parameters(), lr=0.1)
  
  but the user is responsible for updating lr over the course of training.
  ClassyOptimizers extend PyTorch optimizers and allow specifying
  ParamSchedulers instead:
  
      optim = SGD()
      optim.set_param_groups(model.parameters(), lr=LinearParamScheduler(1, 2))
  
  This means that as you step through the optimizer, the learning rate will
  automatically get updated with the given schedule. To access the current
  learning rate value (or any other optimizer option), you can read
  `optim.options_view.lr`. Similar to other Classy abstractions, you can also
  instantiate ClassyOptimizers from a configuration file.
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code: def param_groups(self):
      return self.optimizer.param_groups
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Callable, Dict, Optional
  from classy_vision.generic.util import log_class_usage
  from .param_scheduler import ConstantParamScheduler, ParamScheduler, UpdateInterval
label_desc: Use image node_id 6 for calling the ClassyOptimizer obj's underlying member method code with example usage: obj.param_groups() and returns: self