code: def pack(
      module: LlamaAttention,
      attn_input_scale: float,
      q_output_scale: float,
      k_output_scale: float,
      v_output_scale: float,
      q_rotary_output_scale: float,
      k_rotary_output_scale: float,
      out_input_scale: float,
  ):
      int8_module = LLamaSmoothquantAttention(
          module.hidden_size, module.num_heads
      )
  
      int8_module.attn_input_scale = torch.tensor([attn_input_scale])
  
      int8_module.q_output_scale = torch.tensor([q_output_scale])
      int8_module.k_output_scale = torch.tensor([k_output_scale])
      int8_module.v_output_scale = torch.tensor([v_output_scale])
  
      int8_module.q_rotary_output_scale = torch.tensor(
          [q_rotary_output_scale]
      )
      int8_module.k_rotary_output_scale = torch.tensor(
          [k_rotary_output_scale]
      )
  
      int8_module.q_proj = W8A8B8O8Linear.from_float(
          module.q_proj, attn_input_scale, q_output_scale
      )
      int8_module.k_proj = W8A8B8O8Linear.from_float(
          module.k_proj, attn_input_scale, k_output_scale
      )
      int8_module.v_proj = W8A8B8O8Linear.from_float(
          module.v_proj, attn_input_scale, v_output_scale
      )
      int8_module.o_proj = W8A8BFP32OFP32Linear.from_float(
          module.o_proj, out_input_scale
      )
  
      int8_module.out_input_scale = torch.tensor([out_input_scale])
  
      return int8_module
  
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 LLamaSmoothquantAttention obj's underlying member method code with example usage: obj.pack(module, attn_input_scale, q_output_scale, k_output_scale, v_output_scale, q_rotary_output_scale, k_rotary_output_scale, out_input_scale) and returns: int8_module