code: def forward(self, hidden_states, features=None):
      (
          B,
          n_patch,
          hidden,
      ) = (
          hidden_states.shape
      )  # reshape from (B, n_patch, hidden) to (B, h, w, hidden)
      h, w = int(np.sqrt(n_patch)), int(np.sqrt(n_patch))
      x = hidden_states.transpose([0, 2, 1])
      x = x.reshape([B, hidden, h, w])
      x = self.conv_more(x)
      for i, decoder_block in enumerate(self.blocks):
          if features is not None:
              skip = features[i] if (i < self.n_skip) else None
          else:
              skip = None
          x = decoder_block(x, skip=skip)
      return x
  
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 DecoderCup obj's underlying member method code with example usage: obj.forward(hidden_states, features) and returns: x