class_docstr: Dropout values of some random time points in time series.
  
  Single time points or sub-sequences could be dropped out.
  
  Parameters
  ----------
  p : float, tuple, or list, optional
      Probablity of the value of a time point to be dropped out.
  
      - If float, all series (all channels if `per_channel` is True) have the
        same probability.
      - If list, a series (a channel if `per_channel` is True) has a
        probability sampled from this list randomly.
      - If 2-tuple, a series (a channel if `per_channel` is True) has a
        probability sampled from this interval randomly.
  
      Default: 0.05.
  
  size : int, tuple, or list, optional
      Size of dropped out units.
  
      - If int, all dropped out units have the same size.
      - If list, a dropped out unit has size sampled from this list randomly.
      - If 2-tuple, a dropped out unit has size sampled from this interval
        randomly.
  
      Note that dropped out units could overlap which results in larger units
      effectively, though the probability is low if `p` is small.
  
      Default: 1.
  
  fill : str or float, optional
      How a dropped out value is filled.
  
      - If 'ffill', fill with the last previous value that is not dropped.
      - If 'bfill', fill with the first next value that is not dropped.
      - If 'mean', fill with the mean value of this channel in this series.
      - If float, fill with this value.
  
      Default: 'ffill'.
  
  per_channel : bool, optional
      Whether to sample dropout units independently for each channel in a time
      series or to use the same dropout units for all channels in a time
      series. Default: False.
  
  repeats : int, optional
      The number of times a series is augmented. If greater than one, a series
      will be augmented so many times independently. This parameter can also
      be set by operator `*`. Default: 1.
  
  prob : float, optional
      The probability of a series is augmented. It must be in (0.0, 1.0]. This
      parameter can also be set by operator `@`. Default: 1.0.
  
  seed : int, optional
      The random seed. Default: None.
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code: def __init__(
      self,
      p: Union[float, Tuple[float, float], List[float]] = 0.05,
      size: Union[int, Tuple[int, int], List[int]] = 1,
      fill: Union[str, float] = "ffill",
      per_channel: bool = False,
      repeats: int = 1,
      prob: float = 1.0,
      seed: Optional[int] = _default_seed,
  ):
      self.p = p
      self.size = size
      self.fill = fill
      self.per_channel = per_channel
      super().__init__(repeats=repeats, prob=prob, seed=seed)
  
file_path: tsaug/src/tsaug/_augmenter/dropout.py
imports: from typing import List, Optional, Tuple, Union
  import numpy
  from .base import _Augmenter, _default_seed
label_desc: Use image node_id 1 to create a new Dropout object from inherited base classes: _Augmenter with example: obj = Dropout(p, size, fill, per_channel, repeats, prob, seed)