class_docstr: The DeepSet surrogate is similar to the MLP surrogate but makes predictions
  for ensembles rather than single models.
  
  Currently, it does not support the use of dataset features.
code: def _trainer(self) -> pl.Trainer:
      return pl.Trainer(
          max_epochs=1000,
          logger=False,
          enable_checkpointing=False,
          enable_model_summary=False,
          enable_progress_bar=False,
          gpus=int(torch.cuda.is_available()),
      )
  
imports: from typing import cast, List, Literal, Optional
  import numpy
  import numpy.typing
  import pytorch_lightning
  import torch
  from lightkit.data import DataLoader
  from torch import nn
  from torch.utils.data import TensorDataset
  from tsbench.config import Config, EnsembleConfig
  from tsbench.evaluations.tracking import EnsembleTracker
  from tsbench.surrogate.torch.deepset import DeepSetModel
  from ._base import OutputNormalization, Surrogate
  from ._factory import register_ensemble_surrogate
  from .torch import DeepSetLightningModule, ListMLELoss
  from .transformers import EnsembleConfigTransformer
label_desc: Use image node_id 5 for calling the DeepSetSurrogate obj's underlying member method code with example usage: obj._trainer() and returns: pl