code: def make_input_from_multiple_strings(
      sentence_id: SentenceId, strings: List[str]
  ) -> TranslatorInput:
      """
      Returns a TranslatorInput object from multiple strings, where the first element corresponds to the surface tokens
      and the remaining elements to additional factors. All strings must parse into token sequences of the same length.
  
      :param sentence_id: Sentence id.
      :param strings: A list of strings representing a factored input sequence.
      :return: A TranslatorInput.
      """
      if not bool(strings):
          return TranslatorInput(
              sentence_id=sentence_id, tokens=[], factors=None
          )
  
      tokens = list(utils.get_tokens(strings[0]))
      factors = [
          list(utils.get_tokens(factor)) for factor in strings[1:]
      ]
      if not all(len(factor) == len(tokens) for factor in factors):
          logger.error(
              "Length of string sequences do not match: '%s'", strings
          )
          return _bad_input(sentence_id, reason=str(strings))
      return TranslatorInput(
          sentence_id=sentence_id, tokens=tokens, factors=factors
      )
  
imports: import copy
  import itertools
  import json
  import logging
  from dataclasses import dataclass
  from functools import partial
  from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union
  import numpy
  import torch
  from .None import constants
  from .None import lexicon
  from .None import utils
  from .None import vocab
  from .beam_search import CandidateScorer, get_search_algorithm, GreedySearch, SearchResult
  from .data_io import tokens2ids, get_prepended_token_length
  from .model import SockeyeModel
label_desc: Use image node_id 8 for calling a global function with example usage: make_input_from_multiple_strings(sentence_id, strings) and returns: TranslatorInput, TranslatorInput, _bad_input