class_docstr: NASNetALarge (6 @ 4032) 
class_docstr_tok: ["NASNetALarge","(","6","@","4032",")"]
code: def __init__(
      self,
      num_classes=1000,
      in_chans=3,
      stem_size=96,
      channel_multiplier=2,
      num_features=4032,
      output_stride=32,
      drop_rate=0.0,
      global_pool="avg",
      pad_type="same",
  ):
      super(NASNetALarge, self).__init__()
      self.num_classes = num_classes
      self.stem_size = stem_size
      self.num_features = num_features
      self.channel_multiplier = channel_multiplier
      assert output_stride == 32
  
      channels = self.num_features // 24
      # 24 is default value for the architecture
  
      self.conv0 = ConvNormAct(
          in_channels=in_chans,
          out_channels=self.stem_size,
          kernel_size=3,
          padding=0,
          stride=2,
          norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.1),
          apply_act=False,
      )
  
      self.cell_stem_0 = CellStem0(
          self.stem_size,
          num_channels=channels // (channel_multiplier**2),
          pad_type=pad_type,
      )
      self.cell_stem_1 = CellStem1(
          self.stem_size,
          num_channels=channels // channel_multiplier,
          pad_type=pad_type,
      )
  
      self.cell_0 = FirstCell(
          in_chs_left=channels,
          out_chs_left=channels // 2,
          in_chs_right=2 * channels,
          out_chs_right=channels,
          pad_type=pad_type,
      )
      self.cell_1 = NormalCell(
          in_chs_left=2 * channels,
          out_chs_left=channels,
          in_chs_right=6 * channels,
          out_chs_right=channels,
          pad_type=pad_type,
      )
      self.cell_2 = NormalCell(
          in_chs_left=6 * channels,
          out_chs_left=channels,
          in_chs_right=6 * channels,
          out_chs_right=channels,
          pad_type=pad_type,
      )
      self.cell_3 = NormalCell(
          in_chs_left=6 * channels,
          out_chs_left=channels,
          in_chs_right=6 * channels,
          out_chs_right=channels,
          pad_type=pad_type,
      )
      self.cell_4 = NormalCell(
          in_chs_left=6 * channels,
          out_chs_left=channels,
          in_chs_right=6 * channels,
          out_chs_right=channels,
          pad_type=pad_type,
      )
      self.cell_5 = NormalCell(
          in_chs_left=6 * channels,
          out_chs_left=channels,
          in_chs_right=6 * channels,
          out_chs_right=channels,
          pad_type=pad_type,
      )
  
      self.reduction_cell_0 = ReductionCell0(
          in_chs_left=6 * channels,
          out_chs_left=2 * channels,
          in_chs_right=6 * channels,
          out_chs_right=2 * channels,
          pad_type=pad_type,
      )
      self.cell_6 = FirstCell(
          in_chs_left=6 * channels,
          out_chs_left=channels,
          in_chs_right=8 * channels,
          out_chs_right=2 * channels,
          pad_type=pad_type,
      )
      self.cell_7 = NormalCell(
          in_chs_left=8 * channels,
          out_chs_left=2 * channels,
          in_chs_right=12 * channels,
          out_chs_right=2 * channels,
          pad_type=pad_type,
      )
      self.cell_8 = NormalCell(
          in_chs_left=12 * channels,
          out_chs_left=2 * channels,
          in_chs_right=12 * channels,
          out_chs_right=2 * channels,
          pad_type=pad_type,
      )
      self.cell_9 = NormalCell(
          in_chs_left=12 * channels,
          out_chs_left=2 * channels,
          in_chs_right=12 * channels,
          out_chs_right=2 * channels,
          pad_type=pad_type,
      )
      self.cell_10 = NormalCell(
          in_chs_left=12 * channels,
          out_chs_left=2 * channels,
          in_chs_right=12 * channels,
          out_chs_right=2 * channels,
          pad_type=pad_type,
      )
      self.cell_11 = NormalCell(
          in_chs_left=12 * channels,
          out_chs_left=2 * channels,
          in_chs_right=12 * channels,
          out_chs_right=2 * channels,
          pad_type=pad_type,
      )
  
      self.reduction_cell_1 = ReductionCell1(
          in_chs_left=12 * channels,
          out_chs_left=4 * channels,
          in_chs_right=12 * channels,
          out_chs_right=4 * channels,
          pad_type=pad_type,
      )
      self.cell_12 = FirstCell(
          in_chs_left=12 * channels,
          out_chs_left=2 * channels,
          in_chs_right=16 * channels,
          out_chs_right=4 * channels,
          pad_type=pad_type,
      )
      self.cell_13 = NormalCell(
          in_chs_left=16 * channels,
          out_chs_left=4 * channels,
          in_chs_right=24 * channels,
          out_chs_right=4 * channels,
          pad_type=pad_type,
      )
      self.cell_14 = NormalCell(
          in_chs_left=24 * channels,
          out_chs_left=4 * channels,
          in_chs_right=24 * channels,
          out_chs_right=4 * channels,
          pad_type=pad_type,
      )
      self.cell_15 = NormalCell(
          in_chs_left=24 * channels,
          out_chs_left=4 * channels,
          in_chs_right=24 * channels,
          out_chs_right=4 * channels,
          pad_type=pad_type,
      )
      self.cell_16 = NormalCell(
          in_chs_left=24 * channels,
          out_chs_left=4 * channels,
          in_chs_right=24 * channels,
          out_chs_right=4 * channels,
          pad_type=pad_type,
      )
      self.cell_17 = NormalCell(
          in_chs_left=24 * channels,
          out_chs_left=4 * channels,
          in_chs_right=24 * channels,
          out_chs_right=4 * channels,
          pad_type=pad_type,
      )
      self.act = nn.ReLU(inplace=True)
      self.feature_info = [
          dict(num_chs=96, reduction=2, module="conv0"),
          dict(
              num_chs=168,
              reduction=4,
              module="cell_stem_1.conv_1x1.act",
          ),
          dict(
              num_chs=1008,
              reduction=8,
              module="reduction_cell_0.conv_1x1.act",
          ),
          dict(
              num_chs=2016,
              reduction=16,
              module="reduction_cell_1.conv_1x1.act",
          ),
          dict(num_chs=4032, reduction=32, module="act"),
      ]
  
      (
          self.global_pool,
          self.head_drop,
          self.last_linear,
      ) = create_classifier(
          self.num_features,
          self.num_classes,
          pool_type=global_pool,
          drop_rate=drop_rate,
      )
  
imports: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_classifier
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new NASNetALarge object from inherited base classes: nn with example: obj = NASNetALarge(num_classes, in_chans, stem_size, channel_multiplier, num_features, output_stride, drop_rate, global_pool, pad_type)