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: Return the loss for this batch.
  
  Parameters
  ----------
  y_pred : torch tensor
    Predicted target values
  
  y_true : torch tensor
    True target values.
  
  X : input data, compatible with skorch.dataset.Dataset
    By default, you should be able to pass:
  
      * numpy arrays
      * torch tensors
      * pandas DataFrame or Series
      * scipy sparse CSR matrices
      * a dictionary of the former three
      * a list/tuple of the former three
      * a Dataset
  
    If this doesn't work with your data, you have to pass a
    ``Dataset`` that can deal with the data.
  
  training : bool (default=False)
    Whether train mode should be used or not.
  
  Returns
  -------
  loss : torch Tensor (scalar)
    The loss to be minimized.
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code: def get_loss(self, y_pred, y_true, X=None, training=False):
      """Return the loss for this batch.
  
      Parameters
      ----------
      y_pred : torch tensor
        Predicted target values
  
      y_true : torch tensor
        True target values.
  
      X : input data, compatible with skorch.dataset.Dataset
        By default, you should be able to pass:
  
          * numpy arrays
          * torch tensors
          * pandas DataFrame or Series
          * scipy sparse CSR matrices
          * a dictionary of the former three
          * a list/tuple of the former three
          * a Dataset
  
        If this doesn't work with your data, you have to pass a
        ``Dataset`` that can deal with the data.
  
      training : bool (default=False)
        Whether train mode should be used or not.
  
      Returns
      -------
      loss : torch Tensor (scalar)
        The loss to be minimized.
  
      """
      loss = super().get_loss(y_pred, y_true, X=X, training=training)
      if loss.dim() != 0:
          loss = loss.mean()
      return -loss
  
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 6 for calling the GPBase obj's underlying member method code with example usage: obj.get_loss(y_pred, y_true, X, training) and returns: unknown