code: def __init__(
      self,
      channels,
      pad_type="reflect",
      filt_size=4,
      stride=2,
      pad_off=0,
  ):
      super(BlurPool, self).__init__()
      self.filt_size = filt_size
      self.pad_off = pad_off
      self.pad_sizes = [
          int(1.0 * (filt_size - 1) / 2),
          int(np.ceil(1.0 * (filt_size - 1) / 2)),
          int(1.0 * (filt_size - 1) / 2),
          int(np.ceil(1.0 * (filt_size - 1) / 2)),
      ]
      self.pad_sizes = [
          pad_size + pad_off for pad_size in self.pad_sizes
      ]
      self.stride = stride
      self.off = int((self.stride - 1) / 2.0)
      self.channels = channels
  
      if self.filt_size == 1:
          a = np.array(
              [
                  1.0,
              ]
          )
      elif self.filt_size == 2:
          a = np.array([1.0, 1.0])
      elif self.filt_size == 3:
          a = np.array([1.0, 2.0, 1.0])
      elif self.filt_size == 4:
          a = np.array([1.0, 3.0, 3.0, 1.0])
      elif self.filt_size == 5:
          a = np.array([1.0, 4.0, 6.0, 4.0, 1.0])
      elif self.filt_size == 6:
          a = np.array([1.0, 5.0, 10.0, 10.0, 5.0, 1.0])
      elif self.filt_size == 7:
          a = np.array([1.0, 6.0, 15.0, 20.0, 15.0, 6.0, 1.0])
  
      filt = torch.Tensor(a[:, None] * a[None, :])
      filt = filt / torch.sum(filt)
      self.register_buffer(
          "filt",
          filt[None, None, :, :].repeat((self.channels, 1, 1, 1)),
      )
  
      self.pad = get_pad_layer(pad_type)(self.pad_sizes)
  
file_path: antialiased-cnns/antialiased_cnns/blurpool.py
imports: import torch
  import torch.nn.parallel
  import numpy
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new BlurPool object from inherited base classes: nn with example: obj = BlurPool(channels, pad_type, filt_size, stride, pad_off)