code: def baichuan_mlp_forward(
      self,
      x: torch.Tensor,
  ) -> torch.Tensor:
      x_2d = x.view(-1, x.shape[-1])
      qtype = getattr(self.gate_proj, "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.down_proj(
              linear_q4_0.mlp_forward_xpu(
                  x_2d,
                  self.gate_proj.weight.data,
                  self.up_proj.weight.data,
                  x_2d.shape[0],
                  x_2d.shape[1],
                  self.gate_proj.out_len,
                  qtype,
              )
          )
      return self.down_proj(
          self.act_fn(self.gate_proj(x)) * self.up_proj(x)
      )
  
file_path: BigDL/python/llm/src/bigdl/llm/transformers/models/baichuan2.py
imports: import math
  from typing import Optional, Tuple
  import torch
  import torch.utils.checkpoint
  from torch.nn import functional
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
  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
  from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
  from bigdl.llm.transformers.models.utils import mlp_fusion_check
  from transformers.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: baichuan_mlp_forward(x) and returns: self, self