code: def __init__(
      self,
      module,
      *args,
      likelihood=gpytorch.likelihoods.GaussianLikelihood,
      criterion=gpytorch.mlls.VariationalELBO,
      **kwargs,
  ):
      super().__init__(
          module,
          *args,
          criterion=criterion,
          likelihood=likelihood,
          **kwargs,
      )
  
imports: import pickle
  import re
  import gpytorch
  import numpy
  import torch
  from skorch.net import NeuralNet
  from skorch.dataset import ValidSplit
  from skorch.dataset import unpack_data
  from skorch.callbacks import EpochScoring
  from skorch.callbacks import EpochTimer
  from skorch.callbacks import PassthroughScoring
  from skorch.callbacks import PrintLog
  from skorch.utils import check_is_fitted
  from skorch.utils import get_dim
  from skorch.utils import is_dataset
  from skorch.utils import to_numpy
  from skorch.utils import to_tensor
label_desc: Use image node_id 1 to create a new GPRegressor object from inherited base classes: _GPRegressorPredictMixin, GPBase with example: obj = GPRegressor(module)