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]
class_docstr_tok: ["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 __repr__(self):
      return simple_repr(self)
  
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 8 for calling the LowPassFilter obj's underlying member method code with example usage: obj.__repr__() and returns: simple_repr