Name	Size	Mode	Actions
_multi_tensor/	-	0755	rm
__pycache__/	-	0755	rm
adadelta.py	5228	0644	editdlrm
adadelta.pyi	224	0644	editdlrm
adagrad.py	5297	0644	editdlrm
adagrad.pyi	267	0644	editdlrm
adam.py	7384	0644	editdlrm
adam.pyi	257	0644	editdlrm
adamax.py	5408	0644	editdlrm
adamax.pyi	238	0644	editdlrm
adamw.py	7341	0644	editdlrm
adamw.pyi	258	0644	editdlrm
asgd.py	3377	0644	editdlrm
asgd.pyi	239	0644	editdlrm
lbfgs.py	17240	0644	editdlrm
lbfgs.pyi	336	0644	editdlrm
lr_scheduler.py	69400	0644	editdlrm
lr_scheduler.pyi	2786	0644	editdlrm
nadam.py	6956	0644	editdlrm
nadam.pyi	264	0644	editdlrm
optimizer.py	11877	0644	editdlrm
optimizer.pyi	661	0644	editdlrm
radam.py	6525	0644	editdlrm
radam.pyi	237	0644	editdlrm
rmsprop.py	7351	0644	editdlrm
rmsprop.pyi	267	0644	editdlrm
rprop.py	5410	0644	editdlrm
rprop.pyi	232	0644	editdlrm
sgd.py	6867	0644	editdlrm
sgd.pyi	219	0644	editdlrm
sparse_adam.py	4324	0644	editdlrm
sparse_adam.pyi	218	0644	editdlrm
swa_utils.py	11557	0644	editdlrm
swa_utils.pyi	714	0644	editdlrm
_functional.py	16519	0644	editdlrm
__init__.py	834	0644	editdlrm
__init__.pyi	596	0644	editdlrm

from typing import Iterable, Any, Optional, Callable, Union, List from .optimizer import Optimizer class _LRScheduler: def __init__(self, optimizer: Optimizer, last_epoch: int=...) -> None: ... def state_dict(self) -> dict: ... def load_state_dict(self, state_dict: dict) -> None: ... def get_last_lr(self) -> List[float]: ... def get_lr(self) -> float: ... def step(self, epoch: Optional[int]=...) -> None: ... class LambdaLR(_LRScheduler): def __init__(self, optimizer: Optimizer, lr_lambda: Union[Callable[[int], float], List[Callable[[int], float]]], last_epoch: int=...) -> None: ... class StepLR(_LRScheduler): def __init__(self, optimizer: Optimizer, step_size: int, gamma: float=..., last_epoch: int=...) -> None:... class MultiStepLR(_LRScheduler): def __init__(self, optimizer: Optimizer, milestones: Iterable[int], gamma: float=..., last_epoch: int=...) -> None: ... class ConstantLR(_LRScheduler): def __init__(self, optimizer: Optimizer, factor: float=..., total_iters: int=..., last_epoch: int=...) -> None: ... class LinearLR(_LRScheduler): def __init__(self, optimizer: Optimizer, start_factor: float=..., end_factor: float=..., total_iters: int=..., last_epoch: int=...) -> None: ... class ExponentialLR(_LRScheduler): def __init__(self, optimizer: Optimizer, gamma: float, last_epoch: int=...) -> None: ... class ChainedScheduler(_LRScheduler): def __init__(self, schedulers: List[_LRScheduler]) -> None: ... class SequentialLR(_LRScheduler): def __init__(self, schedulers: List[_LRScheduler], milestones: List[int], last_epoch: int=...) -> None: ... class CosineAnnealingLR(_LRScheduler): def __init__(self, optimizer: Optimizer, T_max: int, eta_min: float=..., last_epoch: int=...) -> None: ... class ReduceLROnPlateau: in_cooldown: bool def __init__(self, optimizer: Optimizer, mode: str=..., factor: float=..., patience: int=..., verbose: bool=..., threshold: float=..., threshold_mode: str=..., cooldown: int=..., min_lr: float=..., eps: float=...) -> None: ... def step(self, metrics: Any, epoch: Optional[int]=...) -> None: ... def state_dict(self) -> dict: ... def load_state_dict(self, state_dict: dict): ... class CyclicLR(_LRScheduler): def __init__(self, optimizer: Optimizer, base_lr: float=..., max_lr: float=..., step_size_up: int=..., step_size_down: int=..., mode: str=..., gamma: float=..., scale_fn: Optional[Callable[[float], float]]=..., scale_mode: str=..., cycle_momentum: bool=..., base_momentum: float=..., max_momentum: float=..., last_epoch: int=...) -> None: ... class CosineAnnealingWarmRestarts(_LRScheduler): def __init__(self, optimizer: Optimizer, T_0: int=..., T_mult: int=..., eta_min: float=..., last_epoch: int=...) -> None: ...