class_docstr: The MLP surrogate predicts a model's performance on a new dataset using an
  MLP.
  
  The MLP converts inputs into feature vectors of the same size and uses
  either ranking or regression to predict metrics.
code: def required_cpus(self) -> int:
      return 4
  
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, ModelConfig
  from tsbench.evaluations.tracking import ModelTracker
  from ._base import DatasetFeaturesMixin, OutputNormalization, Surrogate
  from ._factory import register_surrogate
  from .torch import ListMLELoss, MLPLightningModule
  from .transformers import ConfigTransformer
label_desc: Use image node_id 2 for calling the MLPSurrogate obj's underlying member method code with example usage: obj.required_cpus() and returns: int