class_docstr: analogous to fairseq.data.IndexedCachedDataset
docstr: Get list of tensors of character indices for example i
docstr_tok: ["Get","list","of","tensors","of","character","indices","for","example","i"]
code: def get_chars_list(self, i):
      """Get list of tensors of character indices for example i"""
      result = []
      for word_index in range(
          self.word_offsets[i], self.word_offsets[i + 1]
      ):
          char_indices = self.char_buffer[
              self.char_offsets[word_index] : self.char_offsets[
                  word_index + 1
              ]
          ]
          result.append(torch.from_numpy(char_indices))
      return result
  
imports: from typing import Any, Dict
  import numpy
  import torch
  from fairseq import data, tokenizer
  from fvcore.common.file_io import PathManager
  from pytorch_translate import vocab_constants
  from pytorch_translate.data.dictionary import TAGS
label_desc: Use image node_id 3 for calling the InMemoryNumpyWordCharDataset obj's underlying member method code with example usage: obj.get_chars_list(i) and returns: result