code: def _sanitize_pytorch_types(row_as_dict):
      """Promotes values types in a dictionary to the types supported by pytorch. Raises an error if type is clear error
      if the type can not be promoted.
  
      The parameter is modified in-place.
  
      int8, uint16 are promoted to int32; uint32 -> int64;
      numpy string_, unicode_, object arrays are not supported.
  
      :param dict[str,obj] row_as_dict: a dictionary of key-value pairs. The values types are promoted to
          pytorch compatible.
      :return: None
      """
      for name, value in row_as_dict.items():
          # PyTorch supported types are: double, float, float16, int64, int32, and uint8
          if isinstance(value, np.ndarray):
              if value.dtype == np.int8 and _TORCH_BEFORE_1_1:
                  row_as_dict[name] = value.astype(np.int16)
              elif value.dtype == np.uint16:
                  row_as_dict[name] = value.astype(np.int32)
              elif value.dtype == np.uint32:
                  row_as_dict[name] = value.astype(np.int64)
              elif value.dtype == np.bool_:
                  row_as_dict[name] = value.astype(np.uint8)
              elif re.search("[SaUO]", value.dtype.str):
                  raise TypeError(
                      "Pytorch does not support arrays of string or object classes. "
                      "Found in field {}.".format(name)
                  )
          elif isinstance(value, np.bool_):
              row_as_dict[name] = np.uint8(value)
          elif value is None:
              raise TypeError(
                  "Pytorch does not support nullable fields. Found None in {}".format(
                      name
                  )
              )
  
imports: import collections.abc
  import decimal
  import re
  import logging
  import numpy
  from six import PY2
  from torch.utils.data.dataloader import default_collate
  import torch
  from packaging import version
  from petastorm.reader_impl.shuffling_buffer import RandomShufflingBuffer, NoopShufflingBuffer
  from petastorm.reader_impl.pytorch_shuffling_buffer import BatchedRandomShufflingBuffer, BatchedNoopShufflingBuffer
label_desc: Use image node_id 1 for calling a global function with example usage: _sanitize_pytorch_types(row_as_dict) without return types