class_docstr: analogous to fairseq.data.IndexedCachedDataset
code: def load(self, path):
      with PathManager.open(path, "rb") as f:
          npz = np.load(f)
          if "char_buffer" not in npz or "char_offsets" not in npz:
              raise RuntimeError(
                  f"{path} does not appear to be a word-char dataset!"
              )
          self.word_buffer = npz["word_buffer"]
          self.word_offsets = npz["word_offsets"]
          self.sizes = self.word_offsets[1:] - self.word_offsets[:-1]
          self.char_buffer = npz["char_buffer"]
          self.char_offsets = npz["char_offsets"]
  
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 7 for calling the InMemoryNumpyWordCharDataset obj's underlying member method code with example usage: obj.load(path) without return types