code: def __init__(self, **kwargs):
      super().__init__(
          pretrain_img_size=224,
          embed_dim=96,
          depths=[2, 2, 18, 2],
          num_heads=[3, 6, 12, 24],
          window_size=7,
          drop_path_rate=0.3,
          patch_norm=True,
          **kwargs,
      )
  
      self._out_features = ["res2", "res3", "res4", "res5"]
  
      self._out_feature_strides = {
          "res2": 4,
          "res3": 8,
          "res4": 16,
          "res5": 32,
      }
      self._out_feature_channels = {
          "res2": self.feat_channels[0],
          "res3": self.feat_channels[1],
          "res4": self.feat_channels[2],
          "res5": self.feat_channels[3],
      }
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
  from paddleseg.models.backbones.transformer_utils import *
label_desc: Use image node_id 1 to create a new SwinTransformer_small_patch4_window7_224_maskformer object from inherited base classes: SwinTransformer with example: obj = SwinTransformer_small_patch4_window7_224_maskformer()