code: def __init__(self, stem_size, num_channels=42, pad_type=""):
      super(CellStem0, self).__init__()
      self.num_channels = num_channels
      self.stem_size = stem_size
      self.conv_1x1 = ActConvBn(
          self.stem_size, self.num_channels, 1, stride=1
      )
  
      self.comb_iter_0_left = BranchSeparables(
          self.num_channels, self.num_channels, 5, 2, pad_type
      )
      self.comb_iter_0_right = BranchSeparables(
          self.stem_size,
          self.num_channels,
          7,
          2,
          pad_type,
          stem_cell=True,
      )
  
      self.comb_iter_1_left = create_pool2d(
          "max", 3, 2, padding=pad_type
      )
      self.comb_iter_1_right = BranchSeparables(
          self.stem_size,
          self.num_channels,
          7,
          2,
          pad_type,
          stem_cell=True,
      )
  
      self.comb_iter_2_left = create_pool2d(
          "avg", 3, 2, count_include_pad=False, padding=pad_type
      )
      self.comb_iter_2_right = BranchSeparables(
          self.stem_size,
          self.num_channels,
          5,
          2,
          pad_type,
          stem_cell=True,
      )
  
      self.comb_iter_3_right = create_pool2d(
          "avg", 3, 1, count_include_pad=False, padding=pad_type
      )
  
      self.comb_iter_4_left = BranchSeparables(
          self.num_channels, self.num_channels, 3, 1, pad_type
      )
      self.comb_iter_4_right = create_pool2d(
          "max", 3, 2, padding=pad_type
      )
  
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 CellStem0 object from inherited base classes: nn with example: obj = CellStem0(stem_size, num_channels, pad_type)