class_docstr: The NumPy data type Enum for writing/reading the MMapIndexedDataset indices
      
class_docstr_tok: ["The","NumPy","data","type","Enum","for","writing\/reading","the","MMapIndexedDataset","indices"]
docstr: Get the size of the dtype/code in bytes
  
  Args:
      key (Union[int, Type[numpy.number]]): The dtype or code
  
  Raises:
      ValueError: If the key is neither dtype nor integer code
  
  Returns:
      int: The size of the dtype/code in in bytes
docstr_tok: ["Get","the","size","of","the","dtype\/code","in","bytes","Args",":","key","(","Union","[","int",",","Type","[","numpy.number","]","]",")",":","The","dtype","or","code","Raises",":","ValueError",":","If","the","key","is","neither","dtype","nor","integer","code","Returns",":","int",":","The","size","of","the","dtype\/code","in","in","bytes"]
code: def size(key: Union[int, Type[numpy.number]]) -> int:
      """Get the size of the dtype/code in bytes
  
      Args:
          key (Union[int, Type[numpy.number]]): The dtype or code
  
      Raises:
          ValueError: If the key is neither dtype nor integer code
  
      Returns:
          int: The size of the dtype/code in in bytes
      """
      if isinstance(key, int):
          return DType.dtype_from_code(key)().itemsize
      elif numpy.number in key.__mro__:
          return key().itemsize
      else:
          raise ValueError
  
imports: import logging
  import os
  import shutil
  import struct
  import time
  from enum import Enum
  from functools import lru_cache
  from itertools import accumulate
  from types import TracebackType
  from typing import List, Optional, Tuple, Type, Union
  import numpy
  import torch
  from megatron.core.datasets.utils import log_single_rank
label_desc: Use image node_id 3 for calling the DType obj's underlying member method code with example usage: obj.size(key) and returns: DType, key