code: def test_logistic_regression_ts(self):
      model = LogisticRegression(solver="liblinear")
  
      data = datasets.load_iris()
      X, y = data.data, data.target
      X = X.astype(np.float32)
  
      model.fit(X, y)
  
      ts_model = hummingbird.ml.convert(model, "torch.jit", X)
      self.assertTrue(ts_model is not None)
      np.testing.assert_allclose(
          model.predict(X), ts_model.predict(X), rtol=1e-6, atol=1e-6
      )
      np.testing.assert_allclose(
          model.predict_proba(X),
          ts_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 51 for calling the TestSklearnLinearClassifiers obj's underlying member method code with example usage: obj.test_logistic_regression_ts() without return types