class_docstr: Flat + Cosine lr schedule.
  
  Modified from https://github.com/fastai/fastai/blob/master/fastai/callback/schedule.py#L128 # noqa: E501
  
  Args:
      start_percent (float): When to start annealing the learning rate
          after the percentage of the total training steps.
          The value should be in range [0, 1).
          Default: 0.75
      min_lr (float, optional): The minimum lr. Default: None.
      min_lr_ratio (float, optional): The ratio of minimum lr to the base lr.
          Either `min_lr` or `min_lr_ratio` should be specified.
          Default: None.
code: def __init__(
      self, start_percent=0.75, min_lr=None, min_lr_ratio=None, **kwargs
  ):
      assert (min_lr is None) ^ (min_lr_ratio is None)
      if (
          start_percent < 0
          or start_percent > 1
          or not isinstance(start_percent, float)
      ):
          raise ValueError(
              "expected float between 0 and 1 start_percent, but "
              f"got {start_percent}"
          )
      self.start_percent = start_percent
      self.min_lr = min_lr
      self.min_lr_ratio = min_lr_ratio
      super(FlatCosineAnnealingLrUpdaterHook, self).__init__(**kwargs)
  
imports: import numbers
  from math import cos, pi
  import annotator.uniformer.mmcv
  from .hook import HOOKS, Hook
label_desc: Use image node_id 1 to create a new FlatCosineAnnealingLrUpdaterHook object from inherited base classes: LrUpdaterHook with example: obj = FlatCosineAnnealingLrUpdaterHook(start_percent, min_lr, min_lr_ratio)