code: def should_use_fuse_rope(self, hidden_states, position_ids):
      use_fuse_rope = hidden_states.device.type == "xpu"
      use_fuse_rope = use_fuse_rope and not (
          self.training and hidden_states.requires_grad
      )
      use_fuse_rope = use_fuse_rope and position_ids is not None
      return use_fuse_rope
  
file_path: BigDL/python/llm/src/bigdl/llm/transformers/models/mistral.py
imports: import math
  from typing import Optional, Tuple
  import torch
  from torch import nn
  import torch.nn.functional
  from bigdl.llm.utils.common import invalidInputError
  from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
  from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb, apply_rotary_pos_emb_no_cache_xpu
  from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31, is_enough_kv_cache_room_4_36
  from bigdl.llm.transformers.low_bit_linear import SYM_INT4, FP8E5
  from bigdl.llm.transformers.models.utils import use_flash_attention
label_desc: Use image node_id 2 for calling a global function with example usage: should_use_fuse_rope(hidden_states, position_ids) and returns: use_fuse_rope