class_docstr: analogous to fairseq.data.IndexedCachedDataset
code: def parse(
      self,
      path,
      word_dict,
      char_dict,
      embed_bytes=False,
      reverse_order=False,
      append_eos=False,
  ):
      word_array_list = []
      word_offsets = [0]
      char_array_list = []
      char_offsets = [0]
      sizes = []
      prepend_inds = []
      append_inds = []
      if append_eos:
          append_inds.append(word_dict.eos_index)
      with PathManager.open(path, "r") as f:
          for line in f:
              words, word_inds = self._sent_to_word_ids(
                  sent=line,
                  word_dict=word_dict,
                  reverse_order=reverse_order,
                  prepend_inds=prepend_inds,
                  append_inds=append_inds,
              )
              word_array_list.append(
                  np.array(word_inds, dtype=np.int32)
              )
              word_offsets.append(word_offsets[-1] + len(word_inds))
              sizes.append(len(word_inds))
  
              for word in words:
                  char_inds = self._word_to_char_ids(
                      word, word_dict, char_dict, embed_bytes
                  )
                  char_array_list.append(
                      np.array(char_inds, dtype=np.int32)
                  )
                  char_offsets.append(char_offsets[-1] + len(char_inds))
              if append_eos:
                  char_inds = [char_dict.eos_index]
                  char_array_list.append(
                      np.array(char_inds, dtype=np.int32)
                  )
                  char_offsets.append(char_offsets[-1] + len(char_inds))
  
      self.word_buffer = np.concatenate(word_array_list)
      self.word_offsets = np.array(word_offsets, dtype=np.int64)
      self.char_buffer = np.concatenate(char_array_list)
      self.char_offsets = np.array(char_offsets, dtype=np.int64)
      self.sizes = np.array(sizes, dtype=np.int32)
  
      del (
          word_array_list,
          word_offsets,
          char_array_list,
          char_offsets,
          sizes,
      )
  
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 10 for calling the InMemoryNumpyWordCharDataset obj's underlying member method code with example usage: obj.parse(path, word_dict, char_dict, embed_bytes, reverse_order, append_eos) without return types