class_docstr: Construct the embeddings from patch, position embeddings.
      
class_docstr_tok: ["Construct","the","embeddings","from","patch",",","position","embeddings","."]
code: def forward(self, x):
      x, features = self.hybrid_model(x)
      x = self.patch_embeddings(
          x
      )  # (B, hidden. n_patches^(1/2), n_patches^(1/2))
      x = x.flatten(2)
      x = paddle.transpose(x, [0, 2, 1])
  
      embeddings = x + self.position_embeddings
      embeddings = self.dropout(embeddings)
      return embeddings, 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 Embeddings obj's underlying member method code with example usage: obj.forward(x) and returns: embeddings,  features