class_docstr: Base class for models which incorporate inFairness algorithms.
class_docstr_tok: ["Base","class","for","models","which","incorporate","inFairness","algorithms","."]
docstr: Return class labels for samples in X if task is classification or
  predicted values if task is regression.
  
  Args:
      X (array-like): Test samples.
  
  Returns:
      numpy.ndarray: Test predictions.
  
  See also:
      :meth:`skorch.net.NeuralNet.predict`
docstr_tok: ["Return","class","labels","for","samples","in","X","if","task","is","classification","or","predicted","values","if","task","is","regression",".","Args",":","X","(","array-like",")",":","Test","samples",".","Returns",":","numpy.ndarray",":","Test","predictions",".","See","also",":",":","meth",":","`","skorch.net.NeuralNet.predict","`"]
code: def predict(self, X):
      """Return class labels for samples in X if task is classification or
      predicted values if task is regression.
  
      Args:
          X (array-like): Test samples.
  
      Returns:
          numpy.ndarray: Test predictions.
  
      See also:
          :meth:`skorch.net.NeuralNet.predict`
      """
      if self.regression_:
          return super().predict(X)
      elif hasattr(self, "classes_"):
          return self.classes_[self.predict_proba(X).argmax(axis=1)]
      else:
          return self.predict_proba(X).argmax(axis=1)
  
imports: from inFairness import fairalgo
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.utils.multiclass import type_of_target
  from sklearn.exceptions import NotFittedError
  from skorch import NeuralNet
  from skorch.dataset import unpack_data, Dataset
  from skorch.utils import is_pandas_ndframe
label_desc: Use image node_id 8 for calling the InFairnessNet obj's underlying member method code with example usage: obj.predict(X) and returns: super, self, self