class_docstr: Exponentially increases the learning rate between two boundaries over a number of iterations.
  
  Arguments:
  
      optimizer: wrapped optimizer.
  
      end_lr: the final learning rate.
  
      num_iter: the number of iterations over which the test occurs.
  
      last_epoch: the index of last epoch. Default: -1.
code: def lr(self) -> Union[float, List[float]]:
      return self._lr
  
imports: import importlib
  import logging
  import os
  import uuid
  from copy import deepcopy
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast
  import torch
  from lightning_utilities.core.imports import RequirementCache
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.utilities.types import _TORCH_LRSCHEDULER
  from lightning.pytorch.callbacks import Callback
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.parsing import lightning_hasattr, lightning_setattr
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
  from lightning.pytorch.utilities.types import STEP_OUTPUT, LRScheduler, LRSchedulerConfig
label_desc: Use image node_id 3 for calling the _ExponentialLR obj's underlying member method code with example usage: obj.lr() and returns: self