code: def __init__(self, num_classes=1001):
      super().__init__()
      self.num_classes = num_classes
      self.conv_0 = nn.Sequential(
          OrderedDict(
              [
                  (
                      "conv",
                      nn.Conv2d(
                          3, 96, kernel_size=3, stride=2, bias=False
                      ),
                  ),
                  ("bn", nn.BatchNorm2d(96, eps=0.001)),
              ]
          )
      )
      self.cell_stem_0 = CellStem0(
          in_channels_left=96,
          out_channels_left=54,
          in_channels_right=96,
          out_channels_right=54,
      )
      self.cell_stem_1 = Cell(
          in_channels_left=96,
          out_channels_left=108,
          in_channels_right=270,
          out_channels_right=108,
          match_prev_layer_dimensions=True,
          is_reduction=True,
      )
      self.cell_0 = Cell(
          in_channels_left=270,
          out_channels_left=216,
          in_channels_right=540,
          out_channels_right=216,
          match_prev_layer_dimensions=True,
      )
      self.cell_1 = Cell(
          in_channels_left=540,
          out_channels_left=216,
          in_channels_right=1080,
          out_channels_right=216,
      )
      self.cell_2 = Cell(
          in_channels_left=1080,
          out_channels_left=216,
          in_channels_right=1080,
          out_channels_right=216,
      )
      self.cell_3 = Cell(
          in_channels_left=1080,
          out_channels_left=216,
          in_channels_right=1080,
          out_channels_right=216,
      )
      self.cell_4 = Cell(
          in_channels_left=1080,
          out_channels_left=432,
          in_channels_right=1080,
          out_channels_right=432,
          is_reduction=True,
          zero_pad=True,
      )
      self.cell_5 = Cell(
          in_channels_left=1080,
          out_channels_left=432,
          in_channels_right=2160,
          out_channels_right=432,
          match_prev_layer_dimensions=True,
      )
      self.cell_6 = Cell(
          in_channels_left=2160,
          out_channels_left=432,
          in_channels_right=2160,
          out_channels_right=432,
      )
      self.cell_7 = Cell(
          in_channels_left=2160,
          out_channels_left=432,
          in_channels_right=2160,
          out_channels_right=432,
      )
      self.cell_8 = Cell(
          in_channels_left=2160,
          out_channels_left=864,
          in_channels_right=2160,
          out_channels_right=864,
          is_reduction=True,
      )
      self.cell_9 = Cell(
          in_channels_left=2160,
          out_channels_left=864,
          in_channels_right=4320,
          out_channels_right=864,
          match_prev_layer_dimensions=True,
      )
      self.cell_10 = Cell(
          in_channels_left=4320,
          out_channels_left=864,
          in_channels_right=4320,
          out_channels_right=864,
      )
      self.cell_11 = Cell(
          in_channels_left=4320,
          out_channels_left=864,
          in_channels_right=4320,
          out_channels_right=864,
      )
      self.relu = nn.ReLU()
      self.avg_pool = nn.AvgPool2d(11, stride=1, padding=0)
      self.dropout = nn.Dropout(0.5)
      self.last_linear = nn.Linear(4320, num_classes)
  
imports: from collections import OrderedDict
  import torch
  import torch.nn
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new PNASNet5Large object from inherited base classes: nn with example: obj = PNASNet5Large(num_classes)