code: def create_runner_config_validator():
      validator = ConfigValidator(description="Runner configurations")
  
      validator.add_argument(
          "runner",
          type=str,
          choices=["runner_base", "runner_iter"],
          help="""Runner to use. The "runner_base" uses epoch-based training while iter-based
              runner runs based on iters. Default: runner_base""",
      )
      # add argumetns for training dataset ratios
      validator.add_argument(
          "train_dataset_ratios",
          type=Dict[str, float],
          help="""Ratios of training dataset. This is used in iteration-based runner.
          Do not support for epoch-based runner because how to define an epoch becomes tricky.
          Default: None""",
      )
      validator.add_argument(
          "max_iters",
          type=float,
          help="Maximum number of iterations to run.",
      )
      validator.add_argument(
          "max_epoch",
          type=int,
          help="Maximum number of epochs to run.",
      )
      # add arguments for iters_per_inner_epoch
      validator.add_argument(
          "iters_per_inner_epoch",
          type=float,
          help="Number of iterations per inner epoch. This is required when runner is runner_iter.",
      )
      lr_scheds_choices = registry.list_lr_schedulers()
      validator.add_argument(
          "lr_sched",
          type=str,
          choices=lr_scheds_choices,
          help="Learning rate scheduler to use, from {}".format(
              lr_scheds_choices
          ),
      )
      task_choices = registry.list_tasks()
      validator.add_argument(
          "task",
          type=str,
          choices=task_choices,
          help="Task to use, from {}".format(task_choices),
      )
      # add arguments for init_lr
      validator.add_argument(
          "init_lr",
          type=float,
          help="Initial learning rate. This will be the learning rate after warmup and before decay.",
      )
      # add arguments for min_lr
      validator.add_argument(
          "min_lr",
          type=float,
          help="Minimum learning rate (after decay).",
      )
      # add arguments for warmup_lr
      validator.add_argument(
          "warmup_lr",
          type=float,
          help="Starting learning rate for warmup.",
      )
      # add arguments for learning rate decay rate
      validator.add_argument(
          "lr_decay_rate",
          type=float,
          help="Learning rate decay rate. Required if using a decaying learning rate scheduler.",
      )
      # add arguments for weight decay
      validator.add_argument(
          "weight_decay",
          type=float,
          help="Weight decay rate.",
      )
      # add arguments for training batch size
      validator.add_argument(
          "batch_size_train",
          type=int,
          help="Training batch size.",
      )
      # add arguments for evaluation batch size
      validator.add_argument(
          "batch_size_eval",
          type=int,
          help="Evaluation batch size, including validation and testing.",
      )
      # add arguments for number of workers for data loading
      validator.add_argument(
          "num_workers",
          help="Number of workers for data loading.",
      )
      # add arguments for warm up steps
      validator.add_argument(
          "warmup_steps",
          type=int,
          help="Number of warmup steps. Required if a warmup schedule is used.",
      )
      # add arguments for random seed
      validator.add_argument(
          "seed",
          type=int,
          help="Random seed.",
      )
      # add arguments for output directory
      validator.add_argument(
          "output_dir",
          type=str,
          help="Output directory to save checkpoints and logs.",
      )
      # add arguments for whether only use evaluation
      validator.add_argument(
          "evaluate",
          help="Whether to only evaluate the model. If true, training will not be performed.",
      )
      # add arguments for splits used for training, e.g. ["train", "val"]
      validator.add_argument(
          "train_splits",
          type=list,
          help="Splits to use for training.",
      )
      # add arguments for splits used for validation, e.g. ["val"]
      validator.add_argument(
          "valid_splits",
          type=list,
          help="Splits to use for validation. If not provided, will skip the validation.",
      )
      # add arguments for splits used for testing, e.g. ["test"]
      validator.add_argument(
          "test_splits",
          type=list,
          help="Splits to use for testing. If not provided, will skip the testing.",
      )
      # add arguments for accumulating gradient for iterations
      validator.add_argument(
          "accum_grad_iters",
          type=int,
          help="Number of iterations to accumulate gradient for.",
      )
  
      # ====== distributed training ======
      validator.add_argument(
          "device",
          type=str,
          choices=["cpu", "cuda"],
          help="Device to use. Support 'cuda' or 'cpu' as for now.",
      )
      validator.add_argument(
          "world_size",
          type=int,
          help="Number of processes participating in the job.",
      )
      validator.add_argument("dist_url", type=str)
      validator.add_argument("distributed", type=bool)
      # add arguments to opt using distributed sampler during evaluation or not
      validator.add_argument(
          "use_dist_eval_sampler",
          type=bool,
          help="Whether to use distributed sampler during evaluation or not.",
      )
  
      # ====== task specific ======
      # generation task specific arguments
      # add arguments for maximal length of text output
      validator.add_argument(
          "max_len",
          type=int,
          help="Maximal length of text output.",
      )
      # add arguments for minimal length of text output
      validator.add_argument(
          "min_len",
          type=int,
          help="Minimal length of text output.",
      )
      # add arguments number of beams
      validator.add_argument(
          "num_beams",
          type=int,
          help="Number of beams used for beam search.",
      )
  
      # vqa task specific arguments
      # add arguments for number of answer candidates
      validator.add_argument(
          "num_ans_candidates",
          type=int,
          help="""For ALBEF and BLIP, these models first rank answers according to likelihood to select answer candidates.""",
      )
      # add arguments for inference method
      validator.add_argument(
          "inference_method",
          type=str,
          choices=["genearte", "rank"],
          help="""Inference method to use for question answering. If rank, requires a answer list.""",
      )
  
      # ====== model specific ======
      validator.add_argument(
          "k_test",
          type=int,
          help="Number of top k most similar samples from ITC/VTC selection to be tested.",
      )
  
      return validator
  
imports: import logging
  import json
  from typing import Dict
  from omegaconf import OmegaConf
  from lavis.common.registry import registry
label_desc: Use image node_id 2 for calling a global function with example usage: create_runner_config_validator() and returns: validator