code: def forward(
      self,
      hidden_states: torch.Tensor,
  ):
      x_shape = hidden_states.shape
      gate_out = self.gate_proj(hidden_states)
      up_out = self.up_proj(hidden_states)
      inter_out = gate_out * up_out
      inter_out = (
          inter_out.div_(self.down_proj_input_scale.item())
          .round()
          .clamp(-128, 127)
          .to(torch.int8)
      )
      down_out = self.down_proj(inter_out)
      down_out = down_out.view(*x_shape[:-1], -1)
      return down_out
  
imports: import math
  import os
  import types
  from collections import defaultdict
  from functools import partial
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from torch_int.nn.bmm import BMM_S8T_S8N_F32T, BMM_S8T_S8N_S8T
  from transformers import PreTrainedModel
  from transformers.modeling_outputs import BaseModelOutputWithPast
  from transformers.models.llama.configuration_llama import LlamaConfig
  from transformers.models.llama.modeling_llama import LLAMA_INPUTS_DOCSTRING, LlamaAttention, LlamaDecoderLayer, LlamaMLP, LlamaRotaryEmbedding, repeat_kv, rotate_half
  from transformers.utils import add_start_docstrings_to_model_forward
  from colossalai.inference.tensor_parallel.batch_infer_state import BatchInferState
  from colossalai.kernel.triton import copy_kv_cache_to_dest, int8_rotary_embedding_fwd, smooth_llama_context_attn_fwd, smooth_token_attention_fwd
  from .base_model import BaseSmoothForCausalLM
  from .linear import W8A8B8O8Linear, W8A8BFP32O32LinearSiLU, W8A8BFP32OFP32Linear
label_desc: Use image node_id 3 for calling the LlamaSmoothquantMLP obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: down_out