class_docstr: Torchscriptable version of `BertModel` from Huggingface transformers v2.3.0
  https://github.com/huggingface/transformers/blob/v2.3.0/transformers/modeling_bert.py # noqa
  
  Modifies the `forward` function
  
  Changes to `forward` function ::
      Typings for input, modifications to device, change output type to
      Tuple[Tensor, Tensor, List[Tensor]]
class_docstr_tok: ["Torchscriptable","version","of","`","BertModel","`","from","Huggingface","transformers","v2.3.0","https",":","\/\/github.com\/huggingface\/transformers\/blob\/v2.3.0\/transformers\/modeling_bert.py","#","noqa","Modifies","the","`","forward","`","function","Changes","to","`","forward","`","function",":",":","Typings","for","input",",","modifications","to","device",",","change","output","type","to","Tuple","[","Tensor",",","Tensor",",","List","[","Tensor","]","]"]
code: def __init__(self, config):
      super().__init__(config)
      self.config = config
      self.embeddings = BertEmbeddingsJit(config)
      self.encoder = BertEncoderJit(config)
      self.pooler = BertPooler(config)
  
      self.init_weights()
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from mmf.utils.patch import restore_saved_modules, safecopy_modules
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new BertModelJit object from inherited base classes: BertModel with example: obj = BertModelJit(config)