class_docstr: analogous to fairseq.data.IndexedCachedDataset
docstr: Initialize empty dataset
  Args:
  ignore_chars_for_unks: If true, ignore character sequences of
  unknown words.
docstr_tok: ["Initialize","empty","dataset","Args",":","ignore_chars_for_unks",":","If","true",",","ignore","character","sequences","of","unknown","words","."]
code: def __init__(self, ignore_chars_for_unks=False):
      """
      Initialize empty dataset
      Args:
      ignore_chars_for_unks: If true, ignore character sequences of
      unknown words.
      """
      self.word_buffer = None
      self.word_offsets = None
      self.char_buffer = None
      self.char_offsets = None
      self.ignore_chars_for_unks = ignore_chars_for_unks
      self.sizes = None
  
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 1 to create a new InMemoryNumpyWordCharDataset object from inherited base classes: data.indexed_dataset with example: obj = InMemoryNumpyWordCharDataset(ignore_chars_for_unks)