class_docstr: Same as `LowPassFilters` but applies a single low pass filter.
  
  Shape:
  
      - Input: `[*, T]`
      - Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
  
  >>> lowpass = LowPassFilter(1/4, stride=2)
  >>> x = torch.randn(4, 124)
  >>> list(lowpass(x).shape)
  [4, 62]
code: def cutoff(self):
      return self._lowpasses.cutoffs[0]
  
imports: import math
  from typing import Sequence, Optional
  import torch
  from torch.nn import functional
  from .core import sinc
  from .fftconv import fft_conv1d
  from .utils import simple_repr
label_desc: Use image node_id 2 for calling the LowPassFilter obj's underlying member method code with example usage: obj.cutoff() and returns: self