class_docstr: Represents the abstract base class for learning rate schedulers.
class_docstr_tok: ["Represents","the","abstract","base","class","for","learning","rate","schedulers","."]
docstr: Compute the learning rate of each parameter group.
docstr_tok: ["Compute","the","learning","rate","of","each","parameter","group","."]
code: def _compute_lrs(self) -> List[float]:
      """Compute the learning rate of each parameter group."""
  
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 2 for calling the LRSchedulerBase obj's underlying member method code with example usage: obj._compute_lrs() without return types