code: def qwen_mlp_forward(self, x: torch.Tensor) -> torch.Tensor:
      x_2d = x.view(-1, x.shape[-1])
      qtype = getattr(self.w1, "qtype", None)
      if mlp_fusion_check(x_2d, qtype, self.training):
          import linear_q4_0
  
          if not x_2d.is_contiguous():
              x_2d = x_2d.contiguous()
          return self.c_proj(
              linear_q4_0.mlp_forward_xpu(
                  x_2d,
                  self.w2.weight.data,
                  self.w1.weight.data,
                  x_2d.shape[0],
                  x_2d.shape[1],
                  self.w2.out_len,
                  qtype,
              )
          )
      return self.c_proj(F.silu(self.w2(x)) * self.w1(x))
  
file_path: BigDL/python/llm/src/bigdl/llm/transformers/models/qwen.py
imports: import importlib
  import math
  from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from transformers.utils import logging
  from bigdl.llm.transformers.models.utils import extend_kv_cache, init_kv_cache, append_kv_cache
  from bigdl.llm.transformers.models.utils import init_fp8_kv_cache, extend_fp8_kv_cache, append_fp8_kv_cache, restore_fp8_kv_cache
  from bigdl.llm.transformers.models.utils import rotate_half, quantize_kv_cache
  from bigdl.llm.transformers.models.utils import mlp_fusion_check
  from bigdl.llm.utils.common import invalidInputError, invalidOperationError
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
label_desc: Use image node_id 4 for calling a global function with example usage: qwen_mlp_forward(x) and returns: self, self