class_docstr: Represents the abstract base class for learning rate schedulers.
class_docstr_tok: ["Represents","the","abstract","base","class","for","learning","rate","schedulers","."]
code: def get_lr(self) -> List[float]:  # type: ignore[override]
      if not self._get_lr_called_within_step:  # type: ignore[attr-defined]
          warnings.warn(
              "To get the last learning rate computed by the scheduler, please use `get_last_lr()`."
          )
  
      return self._compute_lrs()
  
imports: import math
  import warnings
  from abc import ABC, abstractmethod
  from typing import List, Sequence, Union, final
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from typing_extensions import TypeAlias
  from fairseq2.typing import finaloverride
label_desc: Use image node_id 1 for calling the LRSchedulerBase obj's underlying member method code with example usage: obj.get_lr() and returns: self