code: def __init__(
      self,
      script_path: str,
      script_args: Optional[Union[list, str]] = None,
      node_rank: int = 1,
      num_nodes: int = 1,
      sanity_serving: bool = False,
      cloud_compute: Optional[CloudCompute] = None,
      parallel: bool = True,
      raise_exception: bool = True,
      env: Optional[Dict[str, Any]] = None,
      **kwargs: Any,
  ):
      super().__init__(
          script_path,
          script_args,
          raise_exception=raise_exception,
          parallel=parallel,
          cloud_compute=cloud_compute,
          **kwargs,
      )
      self.node_rank = node_rank
      self.num_nodes = num_nodes
      self.best_model_path = None
      self.best_model_score = None
      self.monitor = None
      self.sanity_serving = sanity_serving
      self.has_finished = False
      self.env = env
  
imports: import os
  from typing import Any, Dict, List, Optional, Tuple, Type, Union
  from lightning.app.components.python import TracerPythonScript
  from lightning.app.core.flow import LightningFlow
  from lightning.app.storage.path import Path
  from lightning.app.structures import List
  from lightning.app.utilities.app_helpers import Logger
  from lightning.app.utilities.packaging.cloud_compute import CloudCompute
label_desc: Use image node_id 1 to create a new PyTorchLightningScriptRunner object from inherited base classes: TracerPythonScript with example: obj = PyTorchLightningScriptRunner(script_path, script_args, node_rank, num_nodes, sanity_serving, cloud_compute, parallel, raise_exception, env)