code: def __init__(
      self,
      backbone,
      classifier="seg",
      decoder_channels=[256, 128, 64, 16],
      hidden_size=768,
      n_skip=3,
      patches_grid=[14, 14],
      pretrained_path=None,
      skip_channels=[512, 256, 64, 16],
      attention_dropout_rate=0.0,
      dropout_rate=0.1,
      mlp_dim=3072,
      num_heads=12,
      num_layers=12,
      img_size=224,
      num_classes=9,
      zero_head=False,
      vis=False,
  ):
      super(TransUNet, self).__init__()
      self.num_classes = num_classes
      self.zero_head = zero_head
      self.classifier = classifier
      self.transformer = Transformer(
          backbone,
          patches_grid,
          hidden_size,
          dropout_rate,
          num_layers,
          mlp_dim,
          num_heads,
          attention_dropout_rate,
          img_size,
          vis,
      )
      self.decoder = DecoderCup(
          hidden_size, decoder_channels, n_skip, skip_channels
      )
      self.segmentation_head = SegmentationHead(
          in_channels=decoder_channels[-1],
          out_channels=num_classes,
          kernel_size=3,
      )
      if pretrained_path is not None:
          load_pretrained_model(self, pretrained_path)
  
imports: import copy
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from medicalseg.cvlibs import manager
  from medicalseg.utils import load_pretrained_model
label_desc: Use image node_id 1 to create a new TransUNet object from inherited base classes: nn with example: obj = TransUNet(backbone, classifier, decoder_channels, hidden_size, n_skip, patches_grid, pretrained_path, skip_channels, attention_dropout_rate, dropout_rate, mlp_dim, num_heads, num_layers, img_size, num_classes, zero_head, vis)