code: def restore_from_jsonl(
      config: str, file_path: str
  ) -> Union[int, list]:
      """Restores the model output from a JSON Lines (.jsonl) file as a list of lists for the specified model.
      Args:
          config (str): Name of the adapter config to restore the output for.
          file_path (str): Path to the .jsonl file containing the model outputs.
      Returns:
          A list of lists representing the model output for the specified model.
          Returns -1 if there is no output for the specified model in the file.
      """
      # Check if the file exists
      if os.path.exists(file_path):
          # Load content from .jsonl file
          with jsonlines.open(file_path, mode="r") as f:
              data = [line for line in f]
      else:
          raise FileExistsError(
              f"There exists no file at the specified path. \npath:{file_path}"
          )
      # Get result of specified model
      for i, line in enumerate(data):
          if config in line:
              return data[i][config]
      else:
          print(
              f"File does not contain an output for the model {config}."
          )
          return -1
  
imports: import os
  import random
  from typing import Any, Union
  import numpy
  import torch
  from PIL import Image
  from torch import squeeze
  import jsonlines
  import requests
  import transformers
  from adapters import AutoAdapterModel, init
  from transformers import AlbertConfig, BartConfig, BatchEncoding, BeitConfig, BeitImageProcessor, BertConfig, CLIPProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection, DebertaConfig, DebertaV2Config, DistilBertConfig, EncoderDecoderConfig, EncoderDecoderModel, GPT2Config, GPTJConfig, MBartConfig, RobertaConfig, T5Config, ViTConfig, ViTImageProcessor, XLMRobertaConfig
label_desc: Use image node_id 13 for calling a global function with example usage: restore_from_jsonl(config, file_path) and returns: unknown, data