class_docstr: Mixin class that provides a predict method for GP regressors.
class_docstr_tok: ["Mixin","class","that","provides","a","predict","method","for","GP","regressors","."]
docstr: Returns the predicted mean and optionally standard deviation.
  
  Parameters
  ----------
  X : input data
    Input data where the GP is evaluated.
  
  return_std : bool (default=False)
    If True, the standard-deviation of the predictive distribution at the
    query points is returned along with the mean.
  
  return_cov : bool (default=False)
    This exists solely for sklearn compatibility and is not supported by
    skorch.
  
  Returns
  -------
  y_pred : numpy ndarray
    Mean of predictive distribution at the query points.
  
  y_std : numpy ndarray
    Standard deviation of predictive distribution at query points. Only
    returned when ``return_std`` is True.
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code: def predict(self, X, return_std=False, return_cov=False):
      """Returns the predicted mean and optionally standard deviation.
  
      Parameters
      ----------
      X : input data
        Input data where the GP is evaluated.
  
      return_std : bool (default=False)
        If True, the standard-deviation of the predictive distribution at the
        query points is returned along with the mean.
  
      return_cov : bool (default=False)
        This exists solely for sklearn compatibility and is not supported by
        skorch.
  
      Returns
      -------
      y_pred : numpy ndarray
        Mean of predictive distribution at the query points.
  
      y_std : numpy ndarray
        Standard deviation of predictive distribution at query points. Only
        returned when ``return_std`` is True.
  
      """
      if return_cov:
          msg = (
              "The 'return_cov' argument is not supported. Please try: "
              "'posterior = next(gpr.forward_iter(X)); "
              "posterior.covariance_matrix'."
          )
          raise NotImplementedError(msg)
  
      if return_std:
          return self._predict_with_std(X)
      return self._predict(X)
  
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 for calling the _GPRegressorPredictMixin obj's underlying member method code with example usage: obj.predict(X, return_std, return_cov) and returns: self, self