code: def aggregate(self, assignment_weights, x, codewords):
      num_codes, channels = paddle.shape(codewords)
      reshaped_codewords = codewords.reshape(
          [1, 1, num_codes, channels]
      )
      expanded_x = paddle.tile(x.unsqueeze(2), [1, 1, num_codes, 1])
  
      encoded_feat = paddle.multiply(
          assignment_weights.unsqueeze(3),
          (expanded_x - reshaped_codewords),
      ).sum(axis=1)
      encoded_feat = paddle.reshape(
          encoded_feat, [-1, self.num_codes, self.channels]
      )
      return encoded_feat
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the Encoding obj's underlying member method code with example usage: obj.aggregate(assignment_weights, x, codewords) and returns: encoded_feat