class_docstr: Multilingual encoder.
  
  This encoder consists of n separate encoders. A language token ID at the
  begin of the source sentence selects the encoder to use.
class_docstr_tok: ["Multilingual","encoder",".","This","encoder","consists","of","n","separate","encoders",".","A","language","token","ID","at","the","begin","of","the","source","sentence","selects","the","encoder","to","use","."]
docstr: Reorder all outputs according to new_order.
docstr_tok: ["Reorder","all","outputs","according","to","new_order","."]
code: def reorder_encoder_out(self, encoder_out, new_order):
      """Reorder all outputs according to new_order."""
      # assume we can use any of the encoders to do the reordering
      populated_encoders = [
          encoder for encoder in self.encoders if encoder is not None
      ]
      assert len(populated_encoders) > 0
      return populated_encoders[0].reorder_encoder_out(
          encoder_out, new_order
      )
  
imports: import torch
  import torch.nn
  from fairseq.models import FairseqEncoder, FairseqIncrementalDecoder
  from pytorch_translate import utils
  from pytorch_translate.data import data
label_desc: Use image node_id 3 for calling the MultilingualEncoder obj's underlying member method code with example usage: obj.reorder_encoder_out(encoder_out, new_order) and returns: populated_encoders