code: def __init__(self, cell_type, num_cells, num_planes):
      super(PNASNet, self).__init__()
      self.in_planes = num_planes
      self.cell_type = cell_type
  
      self.conv1 = nn.Conv2d(
          3, num_planes, kernel_size=3, stride=1, padding=1, bias=False
      )
      self.bn1 = nn.BatchNorm2d(num_planes)
  
      self.layer1 = self._make_layer(num_planes, num_cells=6)
      self.layer2 = self._downsample(num_planes * 2)
      self.layer3 = self._make_layer(num_planes * 2, num_cells=6)
      self.layer4 = self._downsample(num_planes * 4)
      self.layer5 = self._make_layer(num_planes * 4, num_cells=6)
  
      self.linear = nn.Linear(num_planes * 4, 10)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new PNASNet object from inherited base classes: nn with example: obj = PNASNet(cell_type, num_cells, num_planes)