docstr: Wrapper function for tokenizing query and supports
  Args:
      x (`List[str] or List[List[str]]`):
          List of strings for query or list of lists of strings for supports.
  Returns:
      `transformers.tokenization_utils_base.BatchEncoding` dict with additional keys and values for start_token_id, end_token_id and sizes of example lists for each entity type
docstr_tok: ["Wrapper","function","for","tokenizing","query","and","supports","Args",":","x","(","`","List","[","str","]","or","List","[","List","[","str","]","]","`",")",":","List","of","strings","for","query","or","list","of","lists","of","strings","for","supports",".","Returns",":","`","transformers.tokenization_utils_base.BatchEncoding","`","dict","with","additional","keys","and","values","for","start_token_id",",","end_token_id","and","sizes","of","example","lists","for","each","entity","type"]
code: def tokenize(self, x):
      """
      Wrapper function for tokenizing query and supports
      Args:
          x (`List[str] or List[List[str]]`):
              List of strings for query or list of lists of strings for supports.
      Returns:
          `transformers.tokenization_utils_base.BatchEncoding` dict with additional keys and values for start_token_id, end_token_id and sizes of example lists for each entity type
      """
  
      if isinstance(x, list) and all(isinstance(_x, list) for _x in x):
          d = None
          for l in x:
              t = self.tokenizer(
                  l,
                  padding="max_length",
                  max_length=384,
                  truncation=True,
                  return_tensors="pt",
              )
              t["sizes"] = torch.tensor([len(l)])
              if d is not None:
                  for k in d.keys():
                      d[k] = torch.cat((d[k], t[k]), 0)
              else:
                  d = t
  
          d["start_token_id"] = torch.tensor(
              self.tokenizer.convert_tokens_to_ids("[E]")
          )
          d["end_token_id"] = torch.tensor(
              self.tokenizer.convert_tokens_to_ids("[/E]")
          )
  
      elif isinstance(x, list) and all(isinstance(_x, str) for _x in x):
          d = self.tokenizer(
              x,
              padding="max_length",
              max_length=384,
              truncation=True,
              return_tensors="pt",
          )
  
      else:
          raise Exception(
              "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
              " strings` for supports are supported."
          )
  
      return d
  
imports: import torch
  from transformers import AutoTokenizer
label_desc: Use image node_id 2 for calling the FSNERTokenizerUtils obj's underlying member method code with example usage: obj.tokenize(x) and returns: d