code: def __init__(self, model_name, tokenizer, normalize=True):
      super(AutoModelForSentenceEmbedding, self).__init__()
  
      self.model = AutoModel.from_pretrained(
          model_name
      )  # , load_in_8bit=True, device_map={"":0})
      self.normalize = normalize
      self.tokenizer = tokenizer
  
imports: import argparse
  import logging
  import math
  import os
  import random
  from pathlib import Path
  import datasets
  import evaluate
  import torch
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from datasets import DatasetDict, load_dataset
  from huggingface_hub import Repository, create_repo
  from torch import nn
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from transformers import AutoModel, AutoTokenizer, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils import get_full_repo_name
  from peft import LoraConfig, TaskType, get_peft_model
label_desc: Use image node_id 1 to create a new AutoModelForSentenceEmbedding object from inherited base classes: nn with example: obj = AutoModelForSentenceEmbedding(model_name, tokenizer, normalize)