code: def __init__(
      self,
      backbone,
      grid_size,
      hidden_size,
      dropout_rate,
      num_layers,
      mlp_dim,
      num_heads,
      attention_dropout_rate,
      img_size,
      vis,
  ):
      super(Transformer, self).__init__()
      self.embeddings = Embeddings(
          backbone,
          grid_size,
          hidden_size,
          dropout_rate,
          img_size=img_size,
      )
      self.encoder = Encoder(
          hidden_size,
          num_layers,
          mlp_dim,
          dropout_rate,
          num_heads,
          attention_dropout_rate,
          vis,
      )
  
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 Transformer object from inherited base classes: nn with example: obj = Transformer(backbone, grid_size, hidden_size, dropout_rate, num_layers, mlp_dim, num_heads, attention_dropout_rate, img_size, vis)