code: def get_accelerator():
      global ds_accelerator
      if ds_accelerator is not None:
          return ds_accelerator
  
      accelerator_name = None
      ds_set_method = None
      # 1. Detect whether there is override of DeepSpeed accelerators from environment variable.
      if "DS_ACCELERATOR" in os.environ.keys():
          accelerator_name = os.environ["DS_ACCELERATOR"]
          if accelerator_name == "xpu":
              try:
                  from intel_extension_for_deepspeed import (
                      XPU_Accelerator,
                  )  # noqa: F401 # type: ignore
              except ImportError as e:
                  raise ValueError(
                      f"XPU_Accelerator requires intel_extension_for_deepspeed, which is not installed on this system."
                  )
          elif accelerator_name == "cpu":
              try:
                  import intel_extension_for_pytorch  # noqa: F401 # type: ignore
              except ImportError as e:
                  raise ValueError(
                      f"CPU_Accelerator requires intel_extension_for_pytorch, which is not installed on this system."
                  )
          elif accelerator_name == "npu":
              try:
                  import torch_npu  # noqa: F401 # type: ignore
              except ImportError as e:
                  raise ValueError(
                      f"NPU_Accelerator requires torch_npu, which is not installed on this system."
                  )
              pass
          elif accelerator_name == "mps":
              try:
                  import torch.mps
  
                  # should use torch.mps.is_available() if it exists someday but this is used as proxy
                  torch.mps.current_allocated_memory()
              except (RuntimeError, ImportError) as e:
                  raise ValueError(
                      f"MPS_Accelerator requires torch.mps, which is not installed on this system."
                  )
          elif is_current_accelerator_supported():
              raise ValueError(
                  f"DS_ACCELERATOR must be one of {SUPPORTED_ACCELERATOR_LIST}. "
                  f'Value "{accelerator_name}" is not supported'
              )
          ds_set_method = "override"
  
      # 2. If no override, detect which accelerator to use automatically
      if accelerator_name is None:
          # We need a way to choose among different accelerator types.
          # Currently we detect which accelerator extension is installed
          # in the environment and use it if the installing answer is True.
          # An alternative might be detect whether CUDA device is installed on
          # the system but this comes with two pitfalls:
          # 1. the system may not have torch pre-installed, so
          #    get_accelerator().is_available() may not work.
          # 2. Some scenario like install on login node (without CUDA device)
          #    and run on compute node (with CUDA device) may cause mismatch
          #    between installation time and runtime.
  
          try:
              from intel_extension_for_deepspeed import (
                  XPU_Accelerator,
              )  # noqa: F401,F811 # type: ignore
  
              accelerator_name = "xpu"
          except ImportError as e:
              pass
          if accelerator_name is None:
              try:
                  import intel_extension_for_pytorch  # noqa: F401,F811 # type: ignore
  
                  accelerator_name = "cpu"
              except ImportError as e:
                  pass
          if accelerator_name is None:
              try:
                  import torch_npu  # noqa: F401,F811 # type: ignore
  
                  accelerator_name = "npu"
              except ImportError as e:
                  pass
          if accelerator_name is None:
              try:
                  import torch.mps
  
                  # should use torch.mps.is_available() if it exists someday but this is used as proxy
                  torch.mps.current_allocated_memory()
                  accelerator_name = "mps"
              except (RuntimeError, ImportError) as e:
                  pass
          if accelerator_name is None:
              accelerator_name = "cuda"
  
          ds_set_method = "auto detect"
  
      # 3. Set ds_accelerator accordingly
      if accelerator_name == "cuda":
          from .cuda_accelerator import CUDA_Accelerator
  
          ds_accelerator = CUDA_Accelerator()
      elif accelerator_name == "cpu":
          from .cpu_accelerator import CPU_Accelerator
  
          ds_accelerator = CPU_Accelerator()
      elif accelerator_name == "xpu":
          # XPU_Accelerator is already imported in detection stage
          ds_accelerator = XPU_Accelerator()
      elif accelerator_name == "npu":
          from .npu_accelerator import NPU_Accelerator
  
          ds_accelerator = NPU_Accelerator()
      elif accelerator_name == "mps":
          from .mps_accelerator import MPS_Accelerator
  
          ds_accelerator = MPS_Accelerator()
      _validate_accelerator(ds_accelerator)
      if accel_logger is not None:
          accel_logger.info(
              f"Setting ds_accelerator to {ds_accelerator._name} ({ds_set_method})"
          )
      return ds_accelerator
  
imports: import os
label_desc: Use image node_id 3 for calling a global function with example usage: get_accelerator() and returns: ds_accelerator, ds_accelerator