code: def pre_patch():
      LlamaAttention.forward = LlamaAttention_fast_forward
      LlamaSdpaAttention.forward = LlamaAttention_fast_forward
      LlamaFlashAttention2.forward = LlamaAttention_fast_forward
      LlamaDecoderLayer.forward = LlamaDecoderLayer_fast_forward
      LlamaModel.forward = LlamaModel_fast_forward
      LlamaForCausalLM.forward = LlamaForCausalLM_fast_forward
      PeftModelForCausalLM.forward = PeftModelForCausalLM_fast_forward
      return
  
imports: import torch
  from typing import Optional, Tuple, List, Union
  from torch.nn.functional import scaled_dot_product_attention
  from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
  from transformers.models.llama.modeling_llama import logger, BaseModelOutputWithPast, CausalLMOutputWithPast
  from ..kernels import *
  from ._utils import *
  from ._utils import __version__
  from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel, LlamaForCausalLM
  from peft import PeftModelForCausalLM
  import gc
  import peft
  import bitsandbytes
  import numpy
  import types
  from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, AutoConfig
  from transformers import set_seed
  from peft import LoraConfig, TaskType, get_peft_model
label_desc: Use image node_id 1 for calling the FastLlamaModel obj's underlying member method code with example usage: obj.pre_patch() without return types