code: def forward(self, x):
      if self.training:
          x = paddle.squeeze(x, axis=2)
      else:
          x = paddle.squeeze(x, axis=0)
      x = x.tile([1, 3, 1, 1])
      x, attention_weights, features = self.transformer(
          x
      )  # (B, n_patch, hidden)
      x = self.decoder(x, features)
      logits = self.segmentation_head(x)
      logits = paddle.unsqueeze(logits, axis=2)
      return [logits]
  
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 2 for calling the TransUNet obj's underlying member method code with example usage: obj.forward(x) and returns: list