code: def forward(self, input_ids):
      embedding_output, features = self.embeddings(input_ids)
      encoded, attention_weights = self.encoder(
          embedding_output
      )  # (B, n_patch, hidden)
      return encoded, attention_weights, features
  
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 Transformer obj's underlying member method code with example usage: obj.forward(input_ids) and returns: encoded,  attention_weights,  features