docstr: Make sure we are sharing the input and output embeddings.
  Export to TorchScript can't handle parameter sharing so we are cloning
  them instead.
docstr_tok: ["Make","sure","we","are","sharing","the","input","and","output","embeddings",".","Export","to","TorchScript","ca","n't","handle","parameter","sharing","so","we","are","cloning","them","instead","."]
code: def tie_weights(self):
      """Make sure we are sharing the input and output embeddings.
      Export to TorchScript can't handle parameter sharing so we are cloning
      them instead.
      """
      if hasattr(self, "cls"):
          self._tie_or_clone_weights(
              self.cls.predictions.decoder,
              self.word_embeddings.word_embeddings,
          )
  
imports: import torch
  from mmf.common.registry import registry
  from mmf.models.pythia import Pythia
  from mmf.modules.embeddings import ProjectionEmbedding
  from mmf.utils.transform import transform_to_batch_sequence
  from torch import nn
label_desc: Use image node_id 8 for calling the MMFBert obj's underlying member method code with example usage: obj.tie_weights() without return types