code: def ggml_q_format_convet_xpu2cpu(
      tensor: torch.Tensor, num_elem: int, qtype: int
  ):
      invalidInputError(
          tensor.dtype == torch.uint8, "Input tensor must be uint8"
      )
  
      invalidInputError(
          tensor.device == torch.device("cpu"),
          "Input tensor must be uint8",
      )
  
      src = ctypes.c_void_p(tensor.data.data_ptr())
  
      if qtype in [
          SYM_INT4,
          ASYM_INT4,
          SYM_INT8,
          NF4,
          NF3,
          FP4,
          FP8E4,
          FP8E5,
      ]:
          dst_tensor = torch.empty_like(tensor)
      elif qtype == ggml_tensor_qtype["sym_int5"]:
          QK = ggml.ggml_qk_size(ggml_tensor_qtype["asym_int5"])
          block_size_in_bytes = ggml.ggml_type_size(qtype)
          dst_size = (num_elem // QK) * block_size_in_bytes
          dst_tensor = torch.empty(
              dst_size, dtype=torch.uint8, device=torch.device("cpu")
          )
      else:
          return tensor
      dst = ctypes.c_void_p(dst_tensor.data.data_ptr())
      ggml.ggml_q_format_convet_xpu2cpu(src, dst, num_elem, qtype)
      return dst_tensor
  
imports: from typing import Optional, TypeVar, Union, overload
  from bigdl.llm.utils.common import invalidInputError
  import os
  import torch
  import torch.nn.functional
  from torch import Tensor, device, dtype, nn
  from operator import mul
  from functools import reduce
  from bigdl.llm.transformers.xpu_customize_fwd import custom_fwd, custom_bwd
  from bigdl.llm.transformers.utils import get_autocast_dtype, get_xpu_device_type, get_ipex_version
  import bigdl.llm.ggml.model.llama.llama_cpp
  import ctypes
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
label_desc: Use image node_id 5 for calling a global function with example usage: ggml_q_format_convet_xpu2cpu(tensor, num_elem, qtype) and returns: dst_tensor, tensor