code: def get_precision_context(
      precision: Union[int, str], device_type: Optional[str] = None
  ):
      """
      Choose the proper context manager based on the precision.
  
      Parameters
      ----------
      precision
          The precision.
      device_type
          gpu or cpu.
  
      Returns
      -------
      A precision context manager.
      """
      precision = convert_to_torch_precision(precision=precision)
  
      if precision in [torch.half, torch.float16, torch.bfloat16]:
          return torch.autocast(
              device_type=device_type, dtype=precision
          )
      if precision == torch.float32:
          assert torch.get_default_dtype() == torch.float32
          return contextlib.nullcontext()
      elif precision == torch.float64:
          return double_precision_context()
      else:
          raise ValueError(f"Unknown precision: {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 9 for calling a global function with example usage: get_precision_context(precision, device_type) and returns: torch, contextlib, double_precision_context