The class variable name type is incorrectly defined `CosineAnnealingWarmRestart`

[Lornatang]

pytorch/torch/optim/lr_scheduler.pyi

Line 39 in 2c1b215

def __init__(self, optimizer: Optimizer, T_0: int=..., T_mult: int=..., eta_min: int=..., last_epoch: int=...) -> None: ...

eta_min is defined asint.
But the definition in the original document is float

pytorch/torch/optim/lr_scheduler.py

Line 948 in 2c1b215

eta_min (float, optional): Minimum learning rate. Default: 0.

The complete documentation is defined as follows

class CosineAnnealingWarmRestarts(_LRScheduler):
    r"""Set the learning rate of each parameter group using a cosine annealing
    schedule, where :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}`
    is the number of epochs since the last restart and :math:`T_{i}` is the number
    of epochs between two warm restarts in SGDR:
    .. math::
        \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
        \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right)
    When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`.
    When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`.
    It has been proposed in
    `SGDR: Stochastic Gradient Descent with Warm Restarts`_.
    Args:
        optimizer (Optimizer): Wrapped optimizer.
        T_0 (int): Number of iterations for the first restart.
        T_mult (int, optional): A factor increases :math:`T_{i}` after a restart. Default: 1.
        eta_min (float, optional): Minimum learning rate. Default: 0.
        last_epoch (int, optional): The index of last epoch. Default: -1.
        verbose (bool): If ``True``, prints a message to stdout for
            each update. Default: ``False``.
    .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
        https://arxiv.org/abs/1608.03983
    """

cc @ezyang @malfet @rgommers @xuzhao9 @gramster

[ngimel]

Thank you for reporting, we'll accept a PR fixing this