class_docstr: Parameter Scheduler Base Class
  A scheduler base class that can be used to schedule any optimizer parameter groups.
  
  Unlike the builtin PyTorch schedulers, this is intended to be consistently called
  * At the END of each epoch, before incrementing the epoch count, to calculate next epoch's value
  * At the END of each optimizer update, after incrementing the update count, to calculate next update's value
  
  The schedulers built on this should try to remain as stateless as possible (for simplicity).
  
  This family of schedulers is attempting to avoid the confusion of the meaning of 'last_epoch'
  and -1 values for special behaviour. All epoch and update counts must be tracked in the training
  code and explicitly passed in to the schedulers on the corresponding step or step_update call.
  
  Based on ideas from:
   * https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler
   * https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers
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code: def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
      self.__dict__.update(state_dict)
  
file_path: pytorch-image-models/timm/scheduler/scheduler.py
imports: import abc
  from abc import ABC
  from typing import Any, Dict, Optional
  import torch
label_desc: Use image node_id 3 for calling the Scheduler obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types