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.
      """
      self.bert._tie_or_clone_weights(
          self.cls.predictions.decoder,
          self.bert.embeddings.word_embeddings,
      )
  
imports: import os
  from copy import deepcopy
  from typing import Dict, List, Optional, Tuple
  import torch
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.modules.embeddings import BertVisioLinguisticEmbeddings
  from mmf.modules.hf_layers import BertEncoderJit, BertLayerJit
  from mmf.utils.configuration import get_mmf_cache_dir
  from mmf.utils.modeling import get_optimizer_parameters_for_bert
  from mmf.utils.torchscript import getattr_torchscriptable
  from mmf.utils.transform import transform_to_batch_sequence, transform_to_batch_sequence_dim
  from omegaconf import OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 3 for calling the VisualBERTForPretraining obj's underlying member method code with example usage: obj.tie_weights() without return types