class_docstr: Same as `HighPassFilters` but applies a single high pass filter.
  
  Shape:
  
      - Input: `[*, T]`
      - Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
  
  >>> highpass = HighPassFilter(1/4, stride=1)
  >>> x = torch.randn(4, 124)
  >>> list(highpass(x).shape)
  [4, 124]
code: def __init__(
      self,
      cutoff: float,
      stride: int = 1,
      pad: bool = True,
      zeros: float = 8,
      fft: Optional[bool] = None,
  ):
      super().__init__()
      self._highpasses = HighPassFilters(
          [cutoff], stride, pad, zeros, fft
      )
  
imports: from typing import Sequence, Optional
  import torch
  from .lowpass import lowpass_filter, lowpass_filters, LowPassFilter, LowPassFilters
  from .utils import simple_repr
label_desc: Use image node_id 1 to create a new HighPassFilter object from inherited base classes: torch.nn with example: obj = HighPassFilter(cutoff, stride, pad, zeros, fft)