code: def patch_torch(cuda_to_cpu: bool = True):
      """
      patch_torch is used to patch optimized torch classes to replace original ones.
  
      Optimized classes include:
  
      | 1. pytorch_lightning.Trainer -> bigdl.nano.pytorch.Trainer
      | 2. torchvision.transforms -> bigdl.nano.pytorch.vision.transforms
      | 3. torchvision.datasets -> bigdl.nano.pytorch.vision.datasets
  
      :param cuda_to_cpu: bool, make codes write for CUDA available for CPU if set to True.
             This feature is still experimental and only valid in python layer codes.
             Default to True.
      """
      global is_torch_patched
      if is_torch_patched:
          return
  
      if cuda_to_cpu:
          patch_cuda()
      mapping_torch = _get_patch_map()
  
      for mapping_iter in mapping_torch:
          if mapping_iter[3] is None:
              mapping_iter[3] = getattr(
                  mapping_iter[0], mapping_iter[1], None
              )
          setattr(mapping_iter[0], mapping_iter[1], mapping_iter[2])
  
      is_torch_patched = True
  
imports: import os
  from importlib.util import find_spec
  from bigdl.nano.pytorch.patching.gpu_cpu import patch_cuda, unpatch_cuda, get_cuda_status
label_desc: Use image node_id 2 for calling a global function with example usage: patch_torch(cuda_to_cpu) without return types