code: def should_use_fuse_rope(self, query_states, position_ids):
      use_fuse_rope = query_states.device.type == "xpu"
      use_fuse_rope = use_fuse_rope and not (
          self.training and query_states.requires_grad
      )
      use_fuse_rope = use_fuse_rope and self.config.rope_scaling is None
      use_fuse_rope = use_fuse_rope and position_ids is not None
      return use_fuse_rope
  
imports: import torch
  import warnings
  import importlib
  import torch.nn
  from typing import Optional, Tuple, Union, List
  import math
  import os
  import torch.nn.functional
  from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
  from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31, apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
  from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
  from bigdl.llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
  from bigdl.llm.transformers.models.utils import mlp_fusion_check
  from transformers.modeling_outputs import BaseModelOutputWithPast
  from bigdl.llm.transformers.low_bit_linear import SYM_INT4, FP8E5
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 5 for calling a global function with example usage: should_use_fuse_rope(query_states, position_ids) and returns: use_fuse_rope