code: def get_cosing_embeddings(query_embs, product_embs):
      return torch.sum(query_embs * product_embs, axis=1)
  
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 4 for calling a global function with example usage: get_cosing_embeddings(query_embs, product_embs) and returns: torch