code: def _bf16_rem_to_fp32(
      bf16: torch.Tensor,
      rem: torch.Tensor,
      fp32: torch.Tensor,
  ) -> None:
      """Pack BF16 tensor and 16-bit remainders into FP32 tensor"""
  
      # Check inputs
      assert bf16.size() == rem.size() == fp32.size(), (
          "Tensor dimensions do not match: "
          f"bf16={list(bf16.size())}, "
          f"rem={list(rem.size())}, "
          f"fp32={list(fp32.size())}, "
      )
      assert (
          bf16.dtype is torch.bfloat16
      ), f"bf16 buffer has invalid dtype ({bf16.dtype})"
      assert (
          rem.dtype is torch.int16
      ), f"rem buffer has invalid dtype ({rem.dtype})"
      assert (
          fp32.dtype is torch.float32
      ), f"fp32 buffer has invalid dtype ({fp32.dtype})"
  
      # Undo bf16 rounding
      bf16 = bf16.view(torch.int16) - torch.where(rem < 0, 1, 0)
  
      # Pack bf16 and remainder into little-endian fp32
      fp32 = fp32.unsqueeze(-1).view(torch.int16)
      fp32 = torch.stack((rem, bf16), dim=-1, out=fp32)
  
imports: import collections
  import contextlib
  from dataclasses import dataclass
  import enum
  import inspect
  import io
  import itertools
  import threading
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
  import warnings
  import torch
  from torch.distributed.distributed_c10d import _get_default_group
  from apex.multi_tensor_apply import multi_tensor_applier
  import amp_C
  import distributed_adam_cuda
  from torch.distributed.distributed_c10d import _coalescing_manager
label_desc: Use image node_id 5 for calling a global function with example usage: _bf16_rem_to_fp32(bf16, rem, fp32) without return types