code: def forward(self, x):
      ln_output_fp = torch.nn.functional.layer_norm(
          x, x.shape[-1:], self.weight, None, self.variance_epsilon
      )
      ln_output_int8 = (
          ln_output_fp.round().clamp(-128, 127).to(torch.int8)
      )
      return ln_output_int8
  
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 2 for calling the LlamaLayerNormQ obj's underlying member method code with example usage: obj.forward(x) and returns: ln_output_int8