class_docstr: Base class for models which incorporate inFairness algorithms.
class_docstr_tok: ["Base","class","for","models","which","incorporate","inFairness","algorithms","."]
docstr: Perform a forward step to produce the output used for
  prediction and scoring.
  
  Therefore, the module is set to evaluation mode by default
  beforehand which can be overridden to re-enable features
  like dropout by setting ``training=True``.
  
  Parameters
  ----------
  batch
    A single batch returned by the data loader.
  
  training : bool (default=False)
    Whether to set the module to train mode or not.
  
  Returns
  -------
  y_infer
    The prediction generated by the module.
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code: def evaluation_step(self, batch, training=False):
      """Perform a forward step to produce the output used for
      prediction and scoring.
  
      Therefore, the module is set to evaluation mode by default
      beforehand which can be overridden to re-enable features
      like dropout by setting ``training=True``.
  
      Parameters
      ----------
      batch
        A single batch returned by the data loader.
  
      training : bool (default=False)
        Whether to set the module to train mode or not.
  
      Returns
      -------
      y_infer
        The prediction generated by the module.
  
      """
      return super().evaluation_step(batch, training=training).y_pred
  
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 6 for calling the InFairnessNet obj's underlying member method code with example usage: obj.evaluation_step(batch, training) and returns: super