code: def test_target_encoding(y, criterion, raises):
      """Tests the automatic type casting for classification problems."""
      X = np.random.random((4, 2)).astype("float32")
      y = np.array(y)
      if criterion == nn.MSELoss:
          y = np.array(y, dtype="float32")
      classes = np.unique(y).tolist()
      if criterion == nn.BCEWithLogitsLoss or criterion == nn.MSELoss:
          ndim = 1 if y.ndim < 2 else y.shape[1]
      else:
          ndim = len(classes)
      dx = distances.SquaredEuclideanDistance()
      dx.fit(2)
      dy = distances.SquaredEuclideanDistance()
      dy.fit(ndim)
      mlp = nn.Sequential(
          nn.Linear(2, 10), nn.ReLU(), nn.Linear(10, ndim)
      )
      sensei = SenSeI(
          mlp,
          criterion=criterion,
          distance_x=dx,
          distance_y=dy,
          rho=1.0,
          eps=0.1,
          auditor_nsteps=1,
          auditor_lr=0.01,
          max_epochs=1,
          optimizer=optim.Adam,
          lr=1e-3,
          verbose=0,
      )
      with pytest.raises(
          raises
      ) if raises is not None else nullcontext():
          sensei.fit(X, y)
          assert sensei.regression_ == (criterion == nn.MSELoss)
          if not sensei.regression_:
              assert sensei.classes_.tolist() == classes
  
file_path: AIF360/tests/sklearn/test_infairness.py
imports: from contextlib import nullcontext
  from functools import partial
  from inFairness import fairalgo, distances
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import make_classification, make_regression
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from aif360.sklearn.inprocessing import SenSeI, SenSR
  from aif360.sklearn.inprocessing.infairness import Dataset
label_desc: Use image node_id 3 for calling a global function with example usage: test_target_encoding(y, criterion, raises) without return types