code: def generate_context_manager(
      accelerator=None,
      precision="fp32",
      thread_num=None,
      enable_onednn=False,
      use_xpu=False,
  ):
      """
      generate correct context manager according to different situation
      :param acclerator: str, the accelerator to use, we support "onnxruntime", "openvino",
             "jit", and None for pytorch framework.
      :param precision: str, the precision to use, we support "fp32", "bf16" and "int8".
      :param thread_num: int, the thread number to allocate, None for no limit.
      :param enable_onednn: Whether to use PyTorch JIT graph fuser based on oneDNN Graph
             API, which provides a flexible API for aggressive fusion. Default to
             ``False``, only valid when accelerator="jit", otherwise will be ignored.
      :param use_xpu: if xpu model is used.
      """
      if (
          precision != "bf16" and use_xpu is False
      ) or precision == "fp32":
          return BaseContextManager(
              thread_num=thread_num,
              accelerator=accelerator,
              enable_onednn=enable_onednn,
          )
      elif not use_xpu:
          return AutocastContextManager(
              thread_num=thread_num,
              accelerator=accelerator,
              enable_onednn=enable_onednn,
          )
      else:
          return XPUAutocastContextManager(
              thread_num=thread_num,
              accelerator=accelerator,
              enable_onednn=enable_onednn,
          )
  
imports: import torch
  import operator
  from bigdl.nano.utils.common import compare_version
label_desc: Use image node_id 1 for calling a global function with example usage: generate_context_manager(accelerator, precision, thread_num, enable_onednn, use_xpu) and returns: BaseContextManager, AutocastContextManager, XPUAutocastContextManager