class_docstr: Sensitive Subspace Robustness (SenSR).
  
  SenSR is an in-processing method for individual fairness which enforces
  performance invariance under certain sensitive perturbations to the input
  [#yurochkin19]_.
  
  References:
      .. [#yurochkin19] `M. Yurochkin, A. Bower, and Y. Sun, "Training
         individually fair ML models with sensitive subspace robustness."
         International Conference on Learning Representations, 2020.
         <https://arxiv.org/abs/1907.00020>`_
  
  See also:
      :class:`inFairness.fairalgo.SenSR`
  
  Attributes:
      regression_ (bool): Whether or not this task is treated as regression.
      classes_ (array, shape (n_classes,)): A list of class labels known to
          the transformer. Only present if ``self.regression_`` is False and
          y is provided to ``fit``.
      module_ (inFairness.fairalgo.SenSR): The fair PyTorch module.
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docstr: Initializes the module.
  
  If the module is already initialized and no parameter was changed, it
  will be left as is.
docstr_tok: ["Initializes","the","module",".","If","the","module","is","already","initialized","and","no","parameter","was","changed",",","it","will","be","left","as","is","."]
code: def initialize_module(self):
      """Initializes the module.
  
      If the module is already initialized and no parameter was changed, it
      will be left as is.
      """
      self.initialize_criterion()
      kwargs = self.get_params_for("module")
      network = self.initialized_instance(self.module, kwargs)
  
      sensr_kwargs = {
          "network": network,
          "loss_fn": self.criterion_,
          "distance_x": self.distance_x,
          "eps": self.eps,
          "lr_lamb": self.lr_lamb,
          "lr_param": self.lr_param,
          "auditor_nsteps": self.auditor_nsteps,
          "auditor_lr": self.auditor_lr,
      }
      self.module_ = self.initialized_instance(
          fairalgo.SenSR, sensr_kwargs
      )
      return self
  
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 2 for calling the SenSR obj's underlying member method code with example usage: obj.initialize_module() and returns: self