code: def create(self):
      ngc_url = f"/{self.ngc_org}/team/{self.ngc_team}"
      ngc_cmd = "set -x; conda run --no-capture-output -p /app/env autotrain api --port 7860 --host 0.0.0.0"
      ngc_payload = {
          "name": self.job_name,
          "aceName": self.ngc_ace,
          "aceInstance": self.instance_map[self.backend],
          "dockerImageName": f"{self.ngc_org}/autotrain-advanced:latest",
          "command": ngc_cmd,
          "envs": [
              {"name": key, "value": value}
              for key, value in self.env_vars.items()
          ],
          "jobOrder": 50,
          "jobPriority": "NORMAL",
          "portMappings": [
              {"containerPort": 7860, "protocol": "HTTPS"}
          ],
          "resultContainerMountPoint": "/results",
          "runPolicy": {
              "preemptClass": "RUNONCE",
              "totalRuntimeSeconds": 259200,
          },
      }
  
      ngc_token = self._user_authentication_ngc()
      self._create_ngc_job(ngc_token, ngc_url, ngc_payload)
  
imports: import base64
  import io
  import json
  import os
  import subprocess
  from dataclasses import dataclass
  from typing import Union
  import requests
  from huggingface_hub import HfApi
  from requests.exceptions import HTTPError
  from autotrain import logger
  from autotrain.app_utils import run_training
  from autotrain.trainers.clm.params import LLMTrainingParams
  from autotrain.trainers.dreambooth.params import DreamBoothTrainingParams
  from autotrain.trainers.generic.params import GenericParams
  from autotrain.trainers.image_classification.params import ImageClassificationParams
  from autotrain.trainers.seq2seq.params import Seq2SeqParams
  from autotrain.trainers.tabular.params import TabularParams
  from autotrain.trainers.text_classification.params import TextClassificationParams
label_desc: Use image node_id 4 for calling the NGCRunner obj's underlying member method code with example usage: obj.create() without return types