code: def file(
      # File-based arguments.
      config_file: str = cli.ConfigFile,
      # stopping conditions
      stop: Optional[str] = cli.Stop,
      # Environment override.
      env: Optional[str] = cli.Env,
      # Checkpointing
      checkpoint_freq: int = cli.CheckpointFreq,
      checkpoint_at_end: bool = cli.CheckpointAtEnd,
      keep_checkpoints_num: int = cli.KeepCheckpointsNum,
      checkpoint_score_attr: str = cli.CheckpointScoreAttr,
      # Additional config arguments used for overriding.
      v: bool = cli.V,
      vv: bool = cli.VV,
      framework: FrameworkEnum = cli.Framework,
      trace: bool = cli.Trace,
      # WandB options.
      wandb_key: Optional[str] = cli.WandBKey,
      wandb_project: Optional[str] = cli.WandBProject,
      wandb_run_name: Optional[str] = cli.WandBRunName,
      # Ray cluster options.
      local_mode: bool = cli.LocalMode,
      ray_address: Optional[str] = cli.RayAddress,
      ray_ui: bool = cli.RayUi,
      ray_num_cpus: Optional[int] = cli.RayNumCpus,
      ray_num_gpus: Optional[int] = cli.RayNumGpus,
      ray_num_nodes: Optional[int] = cli.RayNumNodes,
      ray_object_store_memory: Optional[int] = cli.RayObjectStoreMemory,
      # Ray scheduling options.
      resume: bool = cli.Resume,
      scheduler: Optional[str] = cli.Scheduler,
      scheduler_config: str = cli.SchedulerConfig,
  ):
      """Train a reinforcement learning agent from file.
      The file argument is required to run this command.\n\n
  
      Grid search example with the RLlib CLI:\n
        rllib train file tuned_examples/ppo/cartpole-ppo.yaml\n\n
  
      You can also run an example from a URL with the file content:\n
        rllib train file https://raw.githubusercontent.com/ray-project/ray/\
        master/rllib/tuned_examples/ppo/cartpole-ppo.yaml
      """
      # Attempt to download the file if it's not found locally.
      config_file, temp_file = download_example_file(
          example_file=config_file, base_url=None
      )
  
      import_backends()
      framework = framework.value if framework else None
  
      checkpoint_config = {
          "checkpoint_frequency": checkpoint_freq,
          "checkpoint_at_end": checkpoint_at_end,
          "num_to_keep": keep_checkpoints_num,
          "checkpoint_score_attribute": checkpoint_score_attr,
      }
  
      file_type = get_file_type(config_file)
  
      experiments = load_experiments_from_file(
          config_file, file_type, stop, checkpoint_config
      )
      exp_name = list(experiments.keys())[0]
      experiment = experiments[exp_name]
      algo = experiment["run"]
  
      # Override the env from the config by the value given on the command line.
      if env is not None:
          experiment["env"] = env
  
      # WandB logging support.
      callbacks = None
      if wandb_key is not None:
          project = wandb_project or (
              algo.lower()
              + "-"
              + re.sub("\\W+", "-", experiment["env"].lower())
              if file_type == SupportedFileType.python
              else exp_name
          )
          callbacks = [
              WandbLoggerCallback(
                  api_key=wandb_key,
                  project=project,
                  **(
                      {"name": wandb_run_name}
                      if wandb_run_name is not None
                      else {}
                  ),
              )
          ]
  
      # if we had to download the config file, remove the temp file.
      if temp_file:
          temp_file.close()
  
      run_rllib_experiments(
          experiments=experiments,
          v=v,
          vv=vv,
          framework=framework,
          trace=trace,
          ray_num_nodes=ray_num_nodes,
          ray_num_cpus=ray_num_cpus,
          ray_num_gpus=ray_num_gpus,
          ray_object_store_memory=ray_object_store_memory,
          ray_ui=ray_ui,
          ray_address=ray_address,
          local_mode=local_mode,
          resume=resume,
          scheduler=scheduler,
          scheduler_config=scheduler_config,
          algo=algo,
          callbacks=callbacks,
      )
  
imports: import importlib
  import json
  import os
  from pathlib import Path
  import re
  import sys
  import typer
  from typing import Optional
  import uuid
  import yaml
  import ray
  from ray.air.integrations.wandb import WandbLoggerCallback
  from ray.tune.resources import resources_to_json, json_to_resources
  from ray.tune.tune import run_experiments
  from ray.tune.schedulers import create_scheduler
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.common import CLIArguments
  from ray.rllib.common import FrameworkEnum, SupportedFileType
  from ray.rllib.common import download_example_file, get_file_type
label_desc: Use image node_id 4 for calling a global function with example usage: file(config_file, stop, env, checkpoint_freq, checkpoint_at_end, keep_checkpoints_num, checkpoint_score_attr, v, vv, framework, trace, wandb_key, wandb_project, wandb_run_name, local_mode, ray_address, ray_ui, ray_num_cpus, ray_num_gpus, ray_num_nodes, ray_object_store_memory, resume, scheduler, scheduler_config) without return types