code: def forward(self, X):
      # input X is a 4D tensor
      if X.size(1) != self.D:
          raise AssertionError
      B, D = X.size(0), self.D
      if X.dim() == 3:
          # BxDxN -> BxNxD
          X = X.transpose(1, 2).contiguous()
      elif X.dim() == 4:
          # BxDxHxW -> Bx(HW)xD
          X = X.view(B, D, -1).transpose(1, 2).contiguous()
      else:
          raise RuntimeError("Encoding Layer unknown input dims!")
      # assignment weights BxNxK
      A = F.softmax(self.scale_l2(X, self.codewords, self.scale), dim=2)
      # aggregate
      E = self.aggregate(A, X, self.codewords)
      return E
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
  from pywick.models.segmentation.da_basenets.fcn import _FCNHead
label_desc: Use image node_id 3 for calling the Encoding obj's underlying member method code with example usage: obj.forward(X) and returns: E