docstr: convert the sentences into lists of tokens, pad them to the same length, add bos tokens if it is needed
  Args:
      questions (List[str]): list of input questions in str format.
      max_len (int): max number of tokens to generate.
      add_BOS (bool): whether to add the BOS token at the beginning
  Returns:
      Tuple[torch.Tensor], the tokenized and padded torch tensor and the token context length tensor.
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code: def tokenize_batch(self, questions, max_len, add_BOS):
      """
      convert the sentences into lists of tokens, pad them to the same length, add bos tokens if it is needed
      Args:
          questions (List[str]): list of input questions in str format.
          max_len (int): max number of tokens to generate.
          add_BOS (bool): whether to add the BOS token at the beginning
      Returns:
          Tuple[torch.Tensor], the tokenized and padded torch tensor and the token context length tensor.
      """
  
      tokenizer = self.model.tokenizer
  
      # get context from memory
      chunks = []
      first_context = []
      for question in questions:
          hash_code = question
          if hash_code not in self.context_db:
              raise ValueError(f"wrong question is fed: {question}")
          contexts = self.context_db[hash_code]["ctxs"]
          for i, neighbor in enumerate(contexts[: self.neighbors + 1]):
              text = (
                  "title: "
                  + neighbor["title"]
                  + ", source: "
                  + neighbor["text"]
              )
              if i == 0:
                  first_context.append(text)
              tokens = tokenizer.text_to_ids(text)
              tokens = tokens[:128]
              if len(tokens) < 128:
                  tokens = tokens + [tokenizer.eos_id] * (
                      128 - len(tokens)
                  )
              chunks.append(tokens)
      all_lookups = (
          np.array(chunks)
          .reshape(1, self.neighbors + 1, -1)
          .astype(np.int64)
      )
      reuse_neighbors = all_lookups[:, 1:]
      self.store.set("reuse_neighbors", pickle.dumps(reuse_neighbors))
      # combine question and context
      context_tokens = [
          tokenizer.text_to_ids(n + "\nquestion: " + q + " \nanswer:")
          for n, q in zip(first_context, questions)
      ]
  
      if add_BOS:
          context_tokens = [
              [tokenizer.bos_id] + s for s in context_tokens
          ]
      if self.pad_token_for_retrieval:
          padded = []
          for line in context_tokens:
              pad_len = (
                  self.chunk_size - len(line) % self.chunk_size
              ) % self.chunk_size
              padded.append([tokenizer.eos_id] * pad_len + line)
          context_tokens = padded
      context_tokens, context_lengths = pad_batch(
          context_tokens, tokenizer.eos_id, max_len
      )
      context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
      context_length_tensor = torch.cuda.LongTensor(context_lengths)
      return context_tokens_tensor, context_length_tensor
  
imports: import json
  import pickle
  from typing import List, Tuple
  import numpy
  import torch
  import torch.distributed
  from nemo.collections.nlp.modules.common.lm_utils import pad_batch
  from nemo.collections.nlp.modules.common.megatron.retrieval_services.retrieval_service import ComboRetrievalService
  from nemo.collections.nlp.modules.common.text_generation_strategy import TextGenerationStrategy
label_desc: Use image node_id 2 for calling the RetroFileQAModelTextGenerationStrategy obj's underlying member method code with example usage: obj.tokenize_batch(questions, max_len, add_BOS) and returns: context_tokens_tensor,  context_length_tensor