class_docstr: Single band pass filter, implemented as a the difference of two lowpass filters.
  
  Args:
      cutoff_low (float): lower cutoff frequency, 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`.
      cutoff_high (float): higher cutoff frequency, in [0, 0.5] expressed as `f/f_s`.
          This must be higher than cutoff_high. Note that due to the fact
          that filter are not perfect, the output will be non zero even if
          cutoff_high == cutoff_low.
      stride (int): how much to decimate the output.
      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.
  
  
  Shape:
  
      - Input: `[*, T]`
      - Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
  
  ..Note:: There is no BandPassFilters (bank of bandpasses) because its
      signification would be the same as `julius.bands.SplitBands`.
  
  >>> bandpass = BandPassFilter(1/4, 1/3)
  >>> x = torch.randn(4, 12, 21, 1024)
  >>> list(bandpass(x).shape)
  [4, 12, 21, 1024]
code: def cutoff_low(self):
      return self._lowpasses.cutoffs[0]
  
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 2 for calling the BandPassFilter obj's underlying member method code with example usage: obj.cutoff_low() and returns: self