code: def lit_pipeline(
      training_loop: HintOrType[LitModule] = None,
      training_loop_kwargs: OptionalKwargs = None,
      trainer_kwargs: OptionalKwargs = None,
  ) -> None:
      """
      Create a :class:`LitModule` and run :class:`pytorch_lightning.Trainer` with it.
  
      .. note::
          this method modifies the model's parameters in-place.
  
      :param training_loop:
          the training loop or a hint thereof
      :param training_loop_kwargs:
          keyword-based parameters passed to the respective :class:`LitModule` subclass upon instantiation.
      :param trainer_kwargs:
          keyword-based parameters passed to :class:`pytorch_lightning.Trainer`
      """
      pytorch_lightning.Trainer(**(trainer_kwargs or {})).fit(
          model=lit_module_resolver.make(
              training_loop, pos_kwargs=training_loop_kwargs
          )
      )
  
imports: from abc import abstractmethod
  from typing import Optional
  import click
  import pytorch_lightning
  import torch
  import torch.utils.data
  from class_resolver import ClassResolver, HintOrType, OptionalKwargs
  from pykeen.datasets import dataset_resolver, get_dataset
  from pykeen.datasets.base import Dataset
  from pykeen.losses import Loss, loss_resolver
  from pykeen.models import Model, model_resolver
  from pykeen.models.cli import options
  from pykeen.optimizers import optimizer_resolver
  from pykeen.sampling import NegativeSampler
  from pykeen.training import LCWATrainingLoop, SLCWATrainingLoop
  from pykeen.triples.triples_factory import CoreTriplesFactory
  from pykeen.typing import InductiveMode, OneOrSequence
label_desc: Use image node_id 1 for calling a global function with example usage: lit_pipeline(training_loop, training_loop_kwargs, trainer_kwargs) without return types