code: def __init__(self, num_classes=1001):
      super(NASNetALarge, self).__init__()
      self.num_classes = num_classes
  
      self.conv0 = nn.Sequential()
      self.conv0.add_module(
          "conv",
          nn.Conv2d(
              in_channels=3,
              out_channels=96,
              kernel_size=3,
              padding=0,
              stride=2,
              bias=False,
          ),
      )
      self.conv0.add_module(
          "bn", nn.BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True)
      )
  
      self.cell_stem_0 = CellStem0()
      self.cell_stem_1 = CellStem1()
  
      self.cell_0 = FirstCell(
          in_channels_left=168,
          out_channels_left=84,
          in_channels_right=336,
          out_channels_right=168,
      )
      self.cell_1 = NormalCell(
          in_channels_left=336,
          out_channels_left=168,
          in_channels_right=1008,
          out_channels_right=168,
      )
      self.cell_2 = NormalCell(
          in_channels_left=1008,
          out_channels_left=168,
          in_channels_right=1008,
          out_channels_right=168,
      )
      self.cell_3 = NormalCell(
          in_channels_left=1008,
          out_channels_left=168,
          in_channels_right=1008,
          out_channels_right=168,
      )
      self.cell_4 = NormalCell(
          in_channels_left=1008,
          out_channels_left=168,
          in_channels_right=1008,
          out_channels_right=168,
      )
      self.cell_5 = NormalCell(
          in_channels_left=1008,
          out_channels_left=168,
          in_channels_right=1008,
          out_channels_right=168,
      )
  
      self.reduction_cell_0 = ReductionCell0(
          in_channels_left=1008,
          out_channels_left=336,
          in_channels_right=1008,
          out_channels_right=336,
      )
  
      self.cell_6 = FirstCell(
          in_channels_left=1008,
          out_channels_left=168,
          in_channels_right=1344,
          out_channels_right=336,
      )
      self.cell_7 = NormalCell(
          in_channels_left=1344,
          out_channels_left=336,
          in_channels_right=2016,
          out_channels_right=336,
      )
      self.cell_8 = NormalCell(
          in_channels_left=2016,
          out_channels_left=336,
          in_channels_right=2016,
          out_channels_right=336,
      )
      self.cell_9 = NormalCell(
          in_channels_left=2016,
          out_channels_left=336,
          in_channels_right=2016,
          out_channels_right=336,
      )
      self.cell_10 = NormalCell(
          in_channels_left=2016,
          out_channels_left=336,
          in_channels_right=2016,
          out_channels_right=336,
      )
      self.cell_11 = NormalCell(
          in_channels_left=2016,
          out_channels_left=336,
          in_channels_right=2016,
          out_channels_right=336,
      )
  
      self.reduction_cell_1 = ReductionCell1(
          in_channels_left=2016,
          out_channels_left=672,
          in_channels_right=2016,
          out_channels_right=672,
      )
  
      self.cell_12 = FirstCell(
          in_channels_left=2016,
          out_channels_left=336,
          in_channels_right=2688,
          out_channels_right=672,
      )
      self.cell_13 = NormalCell(
          in_channels_left=2688,
          out_channels_left=672,
          in_channels_right=4032,
          out_channels_right=672,
      )
      self.cell_14 = NormalCell(
          in_channels_left=4032,
          out_channels_left=672,
          in_channels_right=4032,
          out_channels_right=672,
      )
      self.cell_15 = NormalCell(
          in_channels_left=4032,
          out_channels_left=672,
          in_channels_right=4032,
          out_channels_right=672,
      )
      self.cell_16 = NormalCell(
          in_channels_left=4032,
          out_channels_left=672,
          in_channels_right=4032,
          out_channels_right=672,
      )
      self.cell_17 = NormalCell(
          in_channels_left=4032,
          out_channels_left=672,
          in_channels_right=4032,
          out_channels_right=672,
      )
  
      self.relu = nn.ReLU()
      self.avg_pool = nn.AvgPool2d(11, stride=1, padding=0)
      self.dropout = nn.Dropout()
      self.last_linear = nn.Linear(4032, self.num_classes)
  
imports: import torch
  import torch.nn
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new NASNetALarge object from inherited base classes: nn with example: obj = NASNetALarge(num_classes)