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: Checks whether the GP is initialized.
  
  Parameters
  ----------
  attributes : iterable of str or None (default=None)
    All the attributes that are strictly required of a fitted net. By
    default, those are the `module_` and `likelihood_` attributes.
  
  Other arguments as in
  ``sklearn.utils.validation.check_is_fitted``.
  
  Raises
  ------
  skorch.exceptions.NotInitializedError
    When the given attributes are not present.
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code: def check_is_fitted(self, attributes=None, *args, **kwargs):
      """Checks whether the GP is initialized.
  
      Parameters
      ----------
      attributes : iterable of str or None (default=None)
        All the attributes that are strictly required of a fitted net. By
        default, those are the `module_` and `likelihood_` attributes.
  
      Other arguments as in
      ``sklearn.utils.validation.check_is_fitted``.
  
      Raises
      ------
      skorch.exceptions.NotInitializedError
        When the given attributes are not present.
  
      """
      if attributes is None:
          attributes = ["module_", "likelihood_"]
      check_is_fitted(self, attributes, *args, **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 4 for calling the GPBase obj's underlying member method code with example usage: obj.check_is_fitted(attributes) without return types