code: def retry_if_cuda_oom(func):
      """
      Makes a function retry itself after encountering
      pytorch's CUDA OOM error.
      It will first retry after calling `torch.cuda.empty_cache()`.
  
      If that still fails, it will then retry by trying to convert inputs to CPUs.
      In this case, it expects the function to dispatch to CPU implementation.
      The return values may become CPU tensors as well and it's user's
      responsibility to convert it back to CUDA tensor if needed.
  
      Args:
          func: a stateless callable that takes tensor-like objects as arguments
  
      Returns:
          a callable which retries `func` if OOM is encountered.
  
      Examples:
      ::
          output = retry_if_cuda_oom(some_torch_function)(input1, input2)
          # output may be on CPU even if inputs are on GPU
  
      Note:
          1. When converting inputs to CPU, it will only look at each argument and check
             if it has `.device` and `.to` for conversion. Nested structures of tensors
             are not supported.
  
          2. Since the function might be called more than once, it has to be
             stateless.
      """
  
      def maybe_to_cpu(x):
          try:
              like_gpu_tensor = x.device.type == "cuda" and hasattr(
                  x, "to"
              )
          except AttributeError:
              like_gpu_tensor = False
          if like_gpu_tensor:
              return x.to(device="cpu")
          else:
              return x
  
      @wraps(func)
      def wrapped(*args, **kwargs):
          with _ignore_torch_cuda_oom():
              return func(*args, **kwargs)
  
          # Clear cache and retry
          torch.cuda.empty_cache()
          with _ignore_torch_cuda_oom():
              return func(*args, **kwargs)
  
          # Try on CPU. This slows down the code significantly, therefore print a notice.
          logger = logging.getLogger(__name__)
          logger.info(
              "Attempting to copy inputs of {} to CPU due to CUDA OOM".format(
                  str(func)
              )
          )
          new_args = (maybe_to_cpu(x) for x in args)
          new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()}
          return func(*new_args, **new_kwargs)
  
      return wrapped
  
imports: import logging
  from contextlib import contextmanager
  from functools import wraps
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: retry_if_cuda_oom(func) and returns: wrapped, func, x, x, func, func