ochs to train for. This is used along
            with steps_per_epoch in order to infer the total number of steps in the cycle
            if a value for total_steps is not provided.
            Default: None
        steps_per_epoch (int): The number of steps per epoch to train for. This is
            used along with epochs in order to infer the total number of steps in the
            cycle if a value for total_steps is not provided.
            Default: None
        pct_start (float): The percentage of the cycle (in number of steps) spent
            increasing the learning rate.
            Default: 0.3
        anneal_strategy (str): {'cos', 'linear'}
            Specifies the annealing strategy: "cos" for cosine annealing, "linear" for
            linear annealing.
            Default: 'cos'
        cycle_momentum (bool): If ``True``, momentum is cycled inversely
            to learning rate between 'base_momentum' and 'max_momentum'.
            Default: True
        base_momentum (float or list): Lower momentum boundaries in the cycle
            for each parameter group. Note that momentum is cycled inversely
            to learning rate; at the peak of a cycle, momentum is
            'base_momentum' and learning rate is 'max_lr'.
            Default: 0.85
        max_momentum (float or list): Upper momentum boundaries in the cycle
            for each parameter group. Functionally,
            it defines the cycle amplitude (max_momentum - base_momentum).
            Note that momentum is cycled inversely
            to learning rate; at the start of a cycle, momentum is 'max_momentum'
            and learning rate is 'base_lr'
            Default: 0.95
        div_factor (float): Determines the initial learning rate via
            initial_lr = max_lr/div_factor
            Default: 25
        final_div_factor (float): Determines the minimum learning rate via
            min_lr = initial_lr/final_div_factor
            Default: 1e4
        three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the
            learning rate according to 'final_div_factor' instead of modifying the second
            phase (the first two phases will be symmetrical about the step indicated by
            'pct_start').
        last_epoch (int): The index of the last batch. This parameter is used when
            resuming a training job. Since `step()` should be invoked after each
            batch instead of after each epoch, this number represents the total
            number of *batches* computed, not the total number of epochs computed.
            When last_epoch=-1, the schedule is started from the beginning.
            Default: -1
        verbose (bool): If ``True``, prints a message to stdout for
            each update. Default: ``False``.

    Example:
        >>> # xdoctest: +SKIP
        >>> data_loader = torch.utils.data.DataLoader(...)
        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
        >>> scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10)
        >>> for epoch in range(10):
        >>>     for batch in data_loader:
        >>>         train_batch(...)
        >>>         scheduler.step()


    .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
        https://arxiv.org/abs/1708.07120
    Nç333333Ó?rå