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","."]
code: def _predict_with_std(self, X):
      nonlin = self._get_predict_nonlinearity()
      y_preds, y_stds = [], []
      for yi in self.forward_iter(X, training=False):
          posterior = yi[0] if isinstance(yi, tuple) else yi
          y_preds.append(to_numpy(nonlin(posterior.mean)))
          y_stds.append(to_numpy(nonlin(posterior.stddev)))
  
      y_pred = np.concatenate(y_preds, 0)
      y_std = np.concatenate(y_stds, 0)
      return y_pred, y_std
  
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 _GPRegressorPredictMixin obj's underlying member method code with example usage: obj._predict_with_std(X) and returns: y_pred,  y_std