parameters: (input, win)
return_statement: return out
docstring: r""" Blur input with 1-D kernel
      Args:
          input (torch.Tensor): a batch of tensors to be blured
          window (torch.Tensor): 1-D gauss kernel
  
      Returns:
          torch.Tensor: blured tensors
docstring_summary: r""" Blur input with 1-D kernel
      Args:
          input (torch.Tensor): a batch of tensors to be blured
          window (torch.Tensor): 1-D gauss kernel
docstring_tokens: r Blur input with 1 - D kernel Args : input ( torch . Tensor ) : a batch of tensors to be blured window ( torch . Tensor ) : 1 - D gauss kernel
function: def gaussian_filter(input, win):
      r""" Blur input with 1-D kernel
      Args:
          input (torch.Tensor): a batch of tensors to be blured
          window (torch.Tensor): 1-D gauss kernel
  
      Returns:
          torch.Tensor: blured tensors
      """
  
      N, C, H, W = input.shape
      out = F.conv2d(input, win, stride=1, padding=0, groups=C)
      out = F.conv2d(out, win.transpose(2, 3), stride=1, padding=0, groups=C)
      return out
function_tokens: def gaussian_filter ( input , win ) : N , C , H , W = input . shape out = F . conv2d ( input , win , stride = 1 , padding = 0 , groups = C ) out = F . conv2d ( out , win . transpose ( 2 , 3 ) , stride = 1 , padding = 0 , groups = C ) return out