class_docstr: Base class for all Gaussian Process estimators.
  
  Most notably, a GPyTorch compatible criterion and likelihood should be
  provided.
class_docstr_tok: ["Base","class","for","all","Gaussian","Process","estimators",".","Most","notably",",","a","GPyTorch","compatible","criterion","and","likelihood","should","be","provided","."]
docstr: Initializes the criterion.
docstr_tok: ["Initializes","the","criterion","."]
code: def initialize_criterion(self):
      """Initializes the criterion."""
      # pylint: disable=attribute-defined-outside-init
  
      # The criterion is always re-initialized here, since it depends on the
      # likelihood and the module.
      criterion_params = self.get_params_for("criterion")
      # criterion takes likelihood as first argument
      self.criterion_ = self.criterion(
          likelihood=self.likelihood_,
          model=self.module_,
          **criterion_params,
      )
      return self
  
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 3 for calling the GPBase obj's underlying member method code with example usage: obj.initialize_criterion() and returns: self