code: def test_modified_huber_multi(self):
      X = np.array(
          [
              [-0.5, -1],
              [-1, -1],
              [-0.1, -0.1],
              [0.1, -0.2],
              [0.5, 1],
              [1, 1],
              [0.1, 0.1],
              [-0.1, 0.2],
          ]
      )
      Y = np.array([0, 1, 1, 1, 2, 2, 2, 2])
  
      model = SGDClassifier(
          loss="modified_huber", max_iter=1000, tol=1e-3
      )
      model.fit(X, Y)
  
      # Use Hummingbird to convert the model to PyTorch
      hb_model = hummingbird.ml.convert(model, "torch")
  
      inputs = [[-1, -1], [1, 1], [-0.2, 0.1], [0.2, -0.1]]
      np.testing.assert_allclose(
          model.predict_proba(inputs),
          hb_model.predict_proba(inputs),
          rtol=1e-6,
          atol=1e-6,
      )
  
imports: import unittest
  import warnings
  from packaging.version import Version, parse
  import numpy
  import torch
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier, LogisticRegressionCV, RidgeCV, Lasso, ElasticNet, Ridge, TweedieRegressor, PoissonRegressor, GammaRegressor
  from sklearn import datasets
  import hummingbird.ml
  from hummingbird.ml._utils import tvm_installed, pandas_installed
  from hummingbird.ml import constants
label_desc: Use image node_id 45 for calling the TestSklearnLinearClassifiers obj's underlying member method code with example usage: obj.test_modified_huber_multi() without return types