class_docstr: Implementation of a configurable command line tool for pytorch-lightning.
class_docstr_tok: ["Implementation","of","a","configurable","command","line","tool","for","pytorch-lightning","."]
docstr: Defines the list of available subcommands and the arguments to skip.
code: def subcommands() -> Dict[str, Set[str]]:
      """Defines the list of available subcommands and the arguments to skip."""
      return {
          "fit": {
              "model",
              "train_dataloaders",
              "val_dataloaders",
              "datamodule",
          },
          "validate": {"model", "dataloaders", "datamodule"},
          "test": {"model", "dataloaders", "datamodule"},
          "predict": {"model", "dataloaders", "datamodule"},
      }
  
imports: import os
  import sys
  from functools import partial, update_wrapper
  from types import MethodType
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
  import torch
  from lightning_utilities.core.imports import RequirementCache
  from lightning_utilities.core.rank_zero import _warn
  from torch.optim import Optimizer
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.utilities.cloud_io import get_filesystem
  from lightning.fabric.utilities.types import _TORCH_LRSCHEDULER
  from lightning.pytorch import Callback, LightningDataModule, LightningModule, Trainer, seed_everything
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
label_desc: Use image node_id 9 for calling the LightningCLI obj's underlying member method code with example usage: obj.subcommands() and returns: dict