class_docstr: Extension of BertTokenizer which supports multiple sentences.
  Separate from normal usecase, each sentence will be passed through
  bert tokenizer separately and indices will be reshaped as single
  tensor. Segment ids will also be increasing in number.
class_docstr_tok: ["Extension","of","BertTokenizer","which","supports","multiple","sentences",".","Separate","from","normal","usecase",",","each","sentence","will","be","passed","through","bert","tokenizer","separately","and","indices","will","be","reshaped","as","single","tensor",".","Segment","ids","will","also","be","increasing","in","number","."]
code: def __init__(self, config, *args, **kwargs):
      super().__init__(config, *args, **kwargs)
      self.fusion_strategy = config.get("fusion", "concat")
      self._probability = config.get("mask_probability", 0)
      self.tokenizer = BertTokenizer(config)
  
imports: import copy
  import random
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from mmf.common.registry import registry
  from mmf.common.sample import Sample, SampleList
  from mmf.datasets.processors.processors import BaseProcessor
label_desc: Use image node_id 1 to create a new MultiSentenceBertTokenizer object from inherited base classes: BaseProcessor with example: obj = MultiSentenceBertTokenizer(config)