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 likelihood and module.
docstr_tok: ["Initializes","likelihood","and","module","."]
code: def initialize_module(self):
      """Initializes likelihood and module."""
      # pylint: disable=attribute-defined-outside-init
      ll_kwargs = self.get_params_for("likelihood")
      self.likelihood_ = self.initialized_instance(
          self.likelihood, ll_kwargs
      )
  
      super().initialize_module()
      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 2 for calling the GPBase obj's underlying member method code with example usage: obj.initialize_module() and returns: self