code: def _main(
      training_loop: HintOrType[LitModule],
      dataset: HintOrType[Dataset],
      create_inverse_triples: bool,
      model: HintOrType[Model],
      loss: HintOrType[Loss],
      batch_size: int,
      embedding_dim: int,
      mixed_precision: bool,
      number_epochs: int,
  ):
      """Run PyTorch lightning model."""
      lit_pipeline(
          training_loop=training_loop,
          training_loop_kwargs=dict(
              dataset=dataset,
              dataset_kwargs=dict(
                  create_inverse_triples=create_inverse_triples
              ),
              model=model,
              model_kwargs=dict(embedding_dim=embedding_dim, loss=loss),
              batch_size=batch_size,
          ),
          trainer_kwargs=dict(
              # automatically choose accelerator
              accelerator="auto",
              # defaults to TensorBoard; explicitly disabled here
              logger=False,
              # disable checkpointing
              enable_checkpointing=False,
              # mixed precision training
              precision=16 if mixed_precision else 32,
              max_epochs=number_epochs,
          ),
      )
  
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 2 for calling a global function with example usage: _main(training_loop, dataset, create_inverse_triples, model, loss, batch_size, embedding_dim, mixed_precision, number_epochs) without return types