code: def infer_precision(
      num_gpus: int,
      precision: Union[int, str],
      as_torch: Optional[bool] = False,
      cpu_only_warning: bool = True,
  ):
      """
      Infer the proper precision based on the environment setup and the provided precision.
  
      Parameters
      ----------
      num_gpus
          GPU number.
      precision
          The precision provided in config.
      as_torch
          Whether to convert the precision to the Pytorch format.
      cpu_only_warning
          Whether to turn on warning if the instance has only CPU.
  
      Returns
      -------
      The inferred precision.
      """
      if num_gpus == 0:  # CPU only prediction
          if cpu_only_warning:
              warnings.warn(
                  "Only CPU is detected in the instance. "
                  "This may result in slow speed for MultiModalPredictor. "
                  "Consider using an instance with GPU support.",
                  UserWarning,
              )
          precision = 32  # Force to use fp32 for training since 16-mixed is not available in CPU
      else:
          if (
              isinstance(precision, str)
              and "bf16" in precision
              and not torch.cuda.is_bf16_supported()
          ):
              warnings.warn(
                  f"{precision} is not supported by the GPU device / cuda version. "
                  "Consider using GPU devices with versions after Amphere or upgrading cuda to be >=11.0. "
                  f"MultiModalPredictor is switching precision from {precision} to 32.",
                  UserWarning,
              )
              precision = 32
  
      if as_torch:
          precision = convert_to_torch_precision(precision=precision)
  
      return precision
  
imports: import contextlib
  import logging
  import math
  import sys
  import warnings
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from lightning.pytorch.accelerators import find_usable_cuda_devices
  from torch import nn
  from autogluon.common.utils.resource_utils import ResourceManager
  from ..constants import DDP_STRATEGIES, OBJECT_DETECTION, OCR
label_desc: Use image node_id 5 for calling a global function with example usage: infer_precision(num_gpus, precision, as_torch, cpu_only_warning) and returns: precision