code: def _test_sgd_classifier(self, num_classes, fit_intercept=True):
      model = SGDClassifier(
          loss="log_loss", fit_intercept=fit_intercept
      )
  
      np.random.seed(0)
      X = np.random.rand(100, 200)
      X = np.array(X, dtype=np.float32)
      y = np.random.randint(num_classes, size=100)
  
      model.fit(X, y)
  
      torch_model = hummingbird.ml.convert(model, "torch")
      self.assertTrue(torch_model is not None)
      np.testing.assert_allclose(
          model.predict_proba(X),
          torch_model.predict_proba(X),
          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 39 for calling the TestSklearnLinearClassifiers obj's underlying member method code with example usage: obj._test_sgd_classifier(num_classes, fit_intercept) without return types