code: def forward(self, x):
      transf_input = self.embed(x)
  
      def rearr(t):
          v_x, v_y, v_z = self.patch_dim
          shape = t.shape
          d = paddle.reshape(t, [shape[0], v_x, v_y, v_z, shape[-1]])
          d = paddle.transpose(d, perm=[0, 4, 1, 2, 3])
          return d
  
      z3, z6, z9, z12 = map(rearr, self.transformer(transf_input))
  
      # Blue convs
      z0 = self.init_conv(x)
      z3 = self.z3_blue_conv(z3)
      z6 = self.z6_blue_conv(z6)
      z9 = self.z9_blue_conv(z9)
  
      # Green block for z12
      z12 = self.z12_deconv(z12)
      # Concat + yellow conv
      y = paddle.concat([z12, z9], axis=1)
      y = self.z9_conv(y)
  
      # Green block for z6
      y = self.z9_deconv(y)
      # Concat + yellow conv
      y = paddle.concat([y, z6], axis=1)
      y = self.z6_conv(y)
  
      # Green block for z3
      y = self.z6_deconv(y)
      # Concat + yellow conv
      y = paddle.concat([y, z3], axis=1)
      y = self.z3_conv(y)
  
      y = self.z3_deconv(y)
      y = paddle.concat([y, z0], axis=1)
      return (self.out_conv(y),)
  
imports: from typing import List, Tuple
  import numpy
  import paddle
  import paddle.nn
  from paddle import Tensor
  from medicalseg.cvlibs import manager
label_desc: Use image node_id 2 for calling the UNETR obj's underlying member method code with example usage: obj.forward(x) and returns: self, d