class_docstr: A simple class that allows us to wrap multiple learning rate schedulers into a single API
  typical of a single learning rate scheduler with a ``step`` method.
  
  Parameters
  ----------
  lr_schedulers: List of dictionaries
      Each dictionary must have key ``scheduler``, which contains the actual learning rate
      scheduler
docstr: Apply ``step`` to all optimizers.
docstr_tok: ["Apply","``","step","``","to","all","optimizers","."]
code: def step(self, *args, **kwargs) -> None:
      """Apply ``step`` to all optimizers."""
      for lr_scheduler in self.lr_schedulers:
          lr_scheduler.step(*args, **kwargs)
  
file_path: collie/collie/model/base/layers.py
imports: from typing import Dict, List
  import torch
label_desc: Use image node_id 2 for calling the MultiLRScheduler obj's underlying member method code with example usage: obj.step() without return types