code: def record_data_in_stream(
      data: T, stream: torch.cuda.streams.Stream
  ) -> None:
      """
      Records the tensor element on certain streams, to avoid memory from being reused for another tensor.
      As mentioned in
      https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html, PyTorch
      uses the "caching allocator" for memory allocation for tensors. When a tensor is
      freed, its memory is likely to be reused by newly constructed tensors. By default,
      this allocator traces whether a tensor is still in use by only the CUDA stream where
      it was created. When a tensor is used by additional CUDA streams, we need to call
      `record_stream` to tell the allocator about these streams. Otherwise, the allocator
      might free the underlying memory of the tensor once it is no longer used by the
      creator stream. This is a notable programming trick when we write programs using
      multiple CUDA streams.
  
      Args:
          data: The data on which to call record_stream
          stream: The CUDA stream with which to call record_stream
      """
  
      # Redundant isinstance(data, tuple) check is required here to make pyre happy
      if _is_named_tuple(data) and isinstance(data, tuple):
          record_data_in_stream(data._asdict(), stream)
      elif isinstance(data, (list, tuple)):
          for e in data:
              record_data_in_stream(e, stream)
      elif isinstance(data, Mapping):
          for _, v in data.items():
              record_data_in_stream(v, stream)
      elif is_dataclass(data) and not isinstance(data, type):
          for field in fields(data):
              record_data_in_stream(getattr(data, field.name), stream)
      elif isinstance(data, _MultistreamableData):
          data.record_stream(stream)
  
imports: import logging
  import os
  import shutil
  import subprocess
  from collections import defaultdict
  from dataclasses import fields, is_dataclass
  from typing import Any, Dict, Mapping, TypeVar
  import torch
  from torchtnt.utils.version import is_torch_version_geq_1_12
  from typing_extensions import Protocol, runtime_checkable, TypedDict
label_desc: Use image node_id 4 for calling a global function with example usage: record_data_in_stream(data, stream) without return types