class_docstr: NASNetAMobile (4 @ 1056) 
class_docstr_tok: ["NASNetAMobile","(","4","@","1056",")"]
code: def __init__(
      self,
      num_classes=1001,
      stem_filters=32,
      penultimate_filters=1056,
      filters_multiplier=2,
  ):
      super(NASNetAMobile, self).__init__()
      self.num_classes = num_classes
      self.stem_filters = stem_filters
      self.penultimate_filters = penultimate_filters
      self.filters_multiplier = filters_multiplier
  
      filters = self.penultimate_filters // 24
      # 24 is default value for the architecture
  
      self.conv0 = nn.Sequential()
      self.conv0.add_module(
          "conv",
          nn.Conv2d(
              in_channels=3,
              out_channels=self.stem_filters,
              kernel_size=3,
              padding=0,
              stride=2,
              bias=False,
          ),
      )
      self.conv0.add_module(
          "bn",
          nn.BatchNorm2d(
              self.stem_filters, eps=0.001, momentum=0.1, affine=True
          ),
      )
  
      self.cell_stem_0 = CellStem0(
          self.stem_filters,
          num_filters=filters // (filters_multiplier**2),
      )
      self.cell_stem_1 = CellStem1(
          self.stem_filters, num_filters=filters // filters_multiplier
      )
  
      self.cell_0 = FirstCell(
          in_channels_left=filters,
          out_channels_left=filters // 2,  # 1, 0.5
          in_channels_right=2 * filters,
          out_channels_right=filters,
      )  # 2, 1
      self.cell_1 = NormalCell(
          in_channels_left=2 * filters,
          out_channels_left=filters,  # 2, 1
          in_channels_right=6 * filters,
          out_channels_right=filters,
      )  # 6, 1
      self.cell_2 = NormalCell(
          in_channels_left=6 * filters,
          out_channels_left=filters,  # 6, 1
          in_channels_right=6 * filters,
          out_channels_right=filters,
      )  # 6, 1
      self.cell_3 = NormalCell(
          in_channels_left=6 * filters,
          out_channels_left=filters,  # 6, 1
          in_channels_right=6 * filters,
          out_channels_right=filters,
      )  # 6, 1
  
      self.reduction_cell_0 = ReductionCell0(
          in_channels_left=6 * filters,
          out_channels_left=2 * filters,  # 6, 2
          in_channels_right=6 * filters,
          out_channels_right=2 * filters,
      )  # 6, 2
  
      self.cell_6 = FirstCell(
          in_channels_left=6 * filters,
          out_channels_left=filters,  # 6, 1
          in_channels_right=8 * filters,
          out_channels_right=2 * filters,
      )  # 8, 2
      self.cell_7 = NormalCell(
          in_channels_left=8 * filters,
          out_channels_left=2 * filters,  # 8, 2
          in_channels_right=12 * filters,
          out_channels_right=2 * filters,
      )  # 12, 2
      self.cell_8 = NormalCell(
          in_channels_left=12 * filters,
          out_channels_left=2 * filters,  # 12, 2
          in_channels_right=12 * filters,
          out_channels_right=2 * filters,
      )  # 12, 2
      self.cell_9 = NormalCell(
          in_channels_left=12 * filters,
          out_channels_left=2 * filters,  # 12, 2
          in_channels_right=12 * filters,
          out_channels_right=2 * filters,
      )  # 12, 2
  
      self.reduction_cell_1 = ReductionCell1(
          in_channels_left=12 * filters,
          out_channels_left=4 * filters,  # 12, 4
          in_channels_right=12 * filters,
          out_channels_right=4 * filters,
      )  # 12, 4
  
      self.cell_12 = FirstCell(
          in_channels_left=12 * filters,
          out_channels_left=2 * filters,  # 12, 2
          in_channels_right=16 * filters,
          out_channels_right=4 * filters,
      )  # 16, 4
      self.cell_13 = NormalCell(
          in_channels_left=16 * filters,
          out_channels_left=4 * filters,  # 16, 4
          in_channels_right=24 * filters,
          out_channels_right=4 * filters,
      )  # 24, 4
      self.cell_14 = NormalCell(
          in_channels_left=24 * filters,
          out_channels_left=4 * filters,  # 24, 4
          in_channels_right=24 * filters,
          out_channels_right=4 * filters,
      )  # 24, 4
      self.cell_15 = NormalCell(
          in_channels_left=24 * filters,
          out_channels_left=4 * filters,  # 24, 4
          in_channels_right=24 * filters,
          out_channels_right=4 * filters,
      )  # 24, 4
  
      self.relu = nn.ReLU()
      self.avg_pool = nn.AvgPool2d(7, stride=1, padding=0)
      self.dropout = nn.Dropout()
      self.last_linear = nn.Linear(24 * filters, 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 NASNetAMobile object from inherited base classes: nn with example: obj = NASNetAMobile(num_classes, stem_filters, penultimate_filters, filters_multiplier)