class_docstr: This class implements a learnable Gaussian lowpass pooling from
  
  Neil Zeghidour, Olivier Teboul, F{'e}lix de Chaumont Quitry & Marco Tagliasacchi, "LEAF: A LEARNABLE FRONTEND
  FOR AUDIO CLASSIFICATION", in Proc. of ICLR 2021 (https://arxiv.org/abs/2101.08596)
  
  Arguments
  ---------
  in_channels : int
      The number of input channels.
  kernel_size: int
      Kernel size of the gaussian lowpass filters.
  stride : int
      Stride factor of the convolutional filters. When the stride factor > 1,
      a decimation in time is performed.
  padding : str
      (same, valid). If "valid", no padding is performed.
      If "same" and stride is 1, output shape is the same as the input shape.
  padding_mode : str
      This flag specifies the type of padding. See torch.nn documentation
      for more information.
  bias : bool
      If True, the additive bias b is adopted.
  skip_transpose : bool
      If False, uses batch x time x channel convention of speechbrain.
      If True, uses batch x channel x time convention.
  
  Example
  -------
  >>> inp_tensor = torch.rand([10, 8000, 40])
  >>> low_pass_pooling = GaussianLowpassPooling(
  ...     40, kernel_size=401, stride=160,
  ... )
  >>> # parameters corresponding to a window of 25 ms and stride 10 ms at 16000 kHz
  >>> out_tensor = low_pass_pooling(inp_tensor)
  >>> out_tensor.shape
  torch.Size([10, 50, 40])
code: def _get_impulse_responses(self, sigma):
      filter_size = self.kernel_size
      sigma = torch.clamp(sigma, min=(2.0 / filter_size), max=0.5)
      t = torch.arange(
          0, filter_size, dtype=sigma.dtype, device=sigma.device
      )
      t = torch.reshape(t, (1, filter_size, 1, 1))
      numerator = t - 0.5 * (filter_size - 1)
      denominator = sigma * 0.5 * (filter_size - 1)
      return torch.exp(-0.5 * (numerator / denominator) ** 2)
  
imports: import torch
  import logging
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the GaussianLowpassPooling obj's underlying member method code with example usage: obj._get_impulse_responses(sigma) and returns: torch