code: def __init__(self):
      super().__init__()
      self.stages = nn.ModuleList()
      for i in range(4):
          stage = nn.Sequential(
              ConvModule(3, 4, kernel_size=1, bias=True)
          )
          self.stages.append(stage)
      self.norm0 = nn.BatchNorm2d(2)
  
      # add some variables to meet unit test coverate rate
      self.cls_token = nn.Parameter(torch.ones(1))
      self.mask_token = nn.Parameter(torch.ones(1))
      self.pos_embed = nn.Parameter(torch.ones(1))
      self.stem_norm = nn.Parameter(torch.ones(1))
      self.downsample_norm0 = nn.BatchNorm2d(2)
      self.downsample_norm1 = nn.BatchNorm2d(2)
      self.downsample_norm2 = nn.BatchNorm2d(2)
      self.lin = nn.Parameter(torch.ones(1))
      self.lin.requires_grad = False
      self.downsample_layers = nn.ModuleList()
      for _ in range(4):
          stage = nn.Sequential(
              nn.Conv2d(3, 4, kernel_size=1, bias=True)
          )
          self.downsample_layers.append(stage)
  
file_path: mmdetection/tests/test_engine/test_optimizers/test_layer_decay_optimizer_constructor.py
imports: import torch
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmdet.engine import LearningRateDecayOptimizerConstructor
label_desc: Use image node_id 1 to create a new ToyConvNeXt object from inherited base classes: nn with example: obj = ToyConvNeXt()