code: def ipex_optimize(
      model: Any,
      optimizers: Any = None,
      dtype: Any = None,
      inplace: bool = False,
      weights_prepack: Any = None,
  ):
      import intel_extension_for_pytorch as ipex
  
      training = model.training
      if optimizers is not None and not isinstance(
          optimizers, (list, tuple)
      ):
          model.train()
          optimizer = optimizers
      elif optimizers is None or len(optimizers) == 0:
          model.eval()
          optimizer = None
      elif len(optimizers) == 1:
          model.train()
          optimizer = optimizers[0]
      else:
          invalidInputError(
              False, "Ipex does not support more than one optimizers."
          )
      ret = ipex.optimize(
          model=model,
          dtype=dtype,
          optimizer=optimizer,
          inplace=inplace,
          weights_prepack=weights_prepack,
      )
      model.train(training)
  
      return ret
  
file_path: BigDL/python/nano/src/bigdl/nano/deps/ipex/ipex_api.py
imports: from typing import Any
  from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 1 for calling a global function with example usage: ipex_optimize(model, optimizers, dtype, inplace, weights_prepack) and returns: ret