class_docstr: Bank of high pass filters. See `julius.lowpass.LowPassFilters` for more
  details on the implementation.
  
  Args:
      cutoffs (list[float]): list of cutoff frequencies, in [0, 0.5] expressed as `f/f_s` where
          f_s is the samplerate and `f` is the cutoff frequency.
          The upper limit is 0.5, because a signal sampled at `f_s` contains only
          frequencies under `f_s / 2`.
      stride (int): how much to decimate the output. Probably not a good idea
          to do so with a high pass filters though...
      pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
          the output will have the same length as the input.
      zeros (float): Number of zero crossings to keep.
          Controls the receptive field of the Finite Impulse Response filter.
          For filters with low cutoff frequency, e.g. 40Hz at 44.1kHz,
          it is a bad idea to set this to a high value.
          This is likely appropriate for most use. Lower values
          will result in a faster filter, but with a slower attenuation around the
          cutoff frequency.
      fft (bool or None): if True, uses `julius.fftconv` rather than PyTorch convolutions.
          If False, uses PyTorch convolutions. If None, either one will be chosen automatically
          depending on the effective filter size.
  
  
  ..warning::
      All the filters will use the same filter size, aligned on the lowest
      frequency provided. If you combine a lot of filters with very diverse frequencies, it might
      be more efficient to split them over multiple modules with similar frequencies.
  
  Shape:
  
      - Input: `[*, T]`
      - Output: `[F, *, T']`, with `T'=T` if `pad` is True and `stride` is 1, and
          `F` is the numer of cutoff frequencies.
  
  >>> highpass = HighPassFilters([1/4])
  >>> x = torch.randn(4, 12, 21, 1024)
  >>> list(highpass(x).shape)
  [1, 4, 12, 21, 1024]
code: def stride(self):
      return self._lowpasses.stride
  
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 3 for calling the HighPassFilters obj's underlying member method code with example usage: obj.stride() and returns: self