code: def batch_to_ids(batch: List[List[str]]) -> torch.Tensor:
      """
      Converts a batch of tokenized sentences to a tensor representing the sentences with encoded characters
      (len(batch), max sentence length, max word length).
  
      # Parameters
  
      batch : `List[List[str]]`, required
          A list of tokenized sentences.
  
      # Returns
  
          A tensor of padded character ids.
      """
      instances = []
      indexer = ELMoTokenCharactersIndexer()
      for sentence in batch:
          tokens = [Token(token) for token in sentence]
          field = TextField(tokens, {"character_ids": indexer})
          instance = Instance({"elmo": field})
          instances.append(instance)
  
      dataset = Batch(instances)
      vocab = Vocabulary()
      dataset.index_instances(vocab)
      return dataset.as_tensor_dict()["elmo"]["character_ids"][
          "elmo_tokens"
      ]
  
imports: import json
  import logging
  import warnings
  from typing import Any, Dict, List, Union
  import numpy
  import torch
  from torch.nn.modules import Dropout
  from allennlp.common import FromParams
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.file_utils import cached_path
  from allennlp.common.util import lazy_groups_of
  from allennlp.data.instance import Instance
  from allennlp.data.tokenizers.token_class import Token
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.data.batch import Batch
  from allennlp.data.fields import TextField
  from allennlp.data.token_indexers.elmo_indexer import ELMoCharacterMapper, ELMoTokenCharactersIndexer
  from allennlp.modules.elmo_lstm import ElmoLstm
  from allennlp.modules.highway import Highway
  from allennlp.modules.scalar_mix import ScalarMix
  from allennlp.nn.util import add_sentence_boundary_token_ids, get_device_of, remove_sentence_boundaries
label_desc: Use image node_id 1 for calling a global function with example usage: batch_to_ids(batch) and returns: dataset