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
code: def __init__(
      self,
      lr_schedulers: List[
          Dict[str, torch.optim.lr_scheduler._LRScheduler]
      ],
  ):
      assert isinstance(
          lr_schedulers, list
      ), f"Expected list, got {type(lr_schedulers)}!"
  
      lr_schedulers_dicts_removed = [
          lr_scheduler["scheduler"] for lr_scheduler in lr_schedulers
      ]
  
      self.lr_schedulers = lr_schedulers_dicts_removed
  
file_path: collie/collie/model/base/layers.py
imports: from typing import Dict, List
  import torch
label_desc: Use image node_id 1 to create a new MultiLRScheduler object from inherited base classes: object with example: obj = MultiLRScheduler(lr_schedulers)