code: def run_rllib_experiments(
      experiments: dict,
      v: cli.V,
      vv: cli.VV,
      framework: str,
      trace: cli.Trace,
      ray_num_nodes: cli.RayNumNodes,
      ray_num_cpus: cli.RayNumCpus,
      ray_num_gpus: cli.RayNumGpus,
      ray_object_store_memory: cli.RayObjectStoreMemory,
      ray_ui: cli.RayUi,
      ray_address: cli.RayAddress,
      local_mode: cli.LocalMode,
      resume: cli.Resume,
      scheduler: cli.Scheduler,
      scheduler_config: cli.SchedulerConfig,
      algo: cli.Algo,
      callbacks=None,
  ):
      """Main training function for the RLlib CLI, whether you've loaded your
      experiments from a config file or from command line options."""
  
      # Override experiment data with command line arguments.
      verbose = 1
      for exp in experiments.values():
          # Bazel makes it hard to find files specified in `args` (and `data`).
          # Look for them here.
          # NOTE: Some of our yaml files don't have a `config` section.
          input_ = exp.get("config", {}).get("input")
          if input_ and input_ != "sampler":
              exp["config"]["input"] = _patch_path(input_)
  
          if not exp.get("env") and not exp.get("config", {}).get(
              "env"
          ):
              raise ValueError(
                  "You either need to provide an --env argument (e.g. 'CartPole-v1') "
                  "or pass an `env` key with a valid environment to your `config`"
                  "argument."
              )
          elif framework is not None:
              exp["config"]["framework"] = framework
          if trace:
              if exp["config"]["framework"] not in ["tf2"]:
                  raise ValueError(
                      "Must enable framework=tf2 to enable eager tracing."
                  )
              exp["config"]["eager_tracing"] = True
          if v:
              exp["config"]["log_level"] = "INFO"
              verbose = 3  # Print details on trial result
          if vv:
              exp["config"]["log_level"] = "DEBUG"
              verbose = 3  # Print details on trial result
  
      # Initialize the Ray cluster with the specified options.
      if ray_num_nodes:
          # Import this only here so that train.py also works with
          # older versions (and user doesn't use `--ray-num-nodes`).
          from ray.cluster_utils import Cluster
  
          cluster = Cluster()
          for _ in range(ray_num_nodes):
              cluster.add_node(
                  num_cpus=ray_num_cpus or 1,
                  num_gpus=ray_num_gpus or 0,
                  object_store_memory=ray_object_store_memory,
              )
          ray.init(address=cluster.address)
      else:
          ray.init(
              include_dashboard=ray_ui,
              address=ray_address,
              object_store_memory=ray_object_store_memory,
              num_cpus=ray_num_cpus,
              num_gpus=ray_num_gpus,
              local_mode=local_mode,
          )
  
      # Run the Tune experiment and return the trials.
      scheduler_config = json.loads(scheduler_config)
      trials = run_experiments(
          experiments,
          scheduler=create_scheduler(scheduler, **scheduler_config),
          resume=resume,
          verbose=verbose,
          concurrent=True,
          callbacks=callbacks,
      )
      ray.shutdown()
  
      checkpoints = []
      for trial in trials:
          if trial.checkpoint:
              checkpoints.append(trial.checkpoint)
  
      if checkpoints:
          from rich import print
          from rich.panel import Panel
  
          print("\nYour training finished.")
  
          print("Best available checkpoint for each trial:")
          for cp in checkpoints:
              print(f"  {cp.path}")
  
          print(
              "\nYou can now evaluate your trained algorithm from any "
              "checkpoint, e.g. by running:"
          )
          print(
              Panel(
                  f"[green]  rllib evaluate {checkpoints[0].path} --algo {algo}"
              )
          )
  
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 6 for calling a global function with example usage: run_rllib_experiments(experiments, v, vv, framework, trace, ray_num_nodes, ray_num_cpus, ray_num_gpus, ray_object_store_memory, ray_ui, ray_address, local_mode, resume, scheduler, scheduler_config, algo, callbacks) without return types