code: def convert_outputs_to_fp32(model_forward):
      model_forward = ConvertOutputsToFp32(model_forward)
  
      def forward(*args, **kwargs):
          return model_forward(*args, **kwargs)
  
      # To act like a decorator so that it can be popped when doing `extract_model_from_parallel`
      forward.__wrapped__ = model_forward
  
      return forward
  
imports: import pickle
  import warnings
  from functools import update_wrapper, wraps
  from typing import Any, Mapping
  import torch
  from ..state import PartialState
  from .constants import TORCH_DISTRIBUTED_OPERATION_TYPES
  from .dataclasses import DistributedType, TensorInformation
  from .imports import is_npu_available, is_torch_distributed_available, is_torch_version, is_tpu_available
label_desc: Use image node_id 29 for calling a global function with example usage: convert_outputs_to_fp32(model_forward) and returns: forward, model_forward