code: def forward(self, x):
      x_dims = x.ndim
      assert (
          x_dims == 4
      ), "The dimension of input tensor must equal 4, but got {}.".format(
          x_dims
      )
      assert (
          paddle.shape(x)[1] == self.channels
      ), "Encoding channels error, excepted {} but got {}.".format(
          self.channels, paddle.shape(x)[1]
      )
      batch_size = paddle.shape(x)[0]
      x = x.reshape([batch_size, self.channels, -1]).transpose(
          [0, 2, 1]
      )
      assignment_weights = F.softmax(
          self.scaled_l2(x, self.codewords, self.scale), axis=2
      )
  
      encoded_feat = self.aggregate(
          assignment_weights, x, self.codewords
      )
      encoded_feat = encoded_feat.reshape(
          [batch_size, self.num_codes, -1]
      )
      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 4 for calling the Encoding obj's underlying member method code with example usage: obj.forward(x) and returns: encoded_feat