code: def test_logistic_regression_pandas(self):
      model = LogisticRegression(solver="liblinear")
  
      data = datasets.load_iris()
      X, y = data.data[:, :3], data.target
      X = X.astype(np.float32)
      X_train = pandas.DataFrame(X, columns=["vA", "vB", "vC"])
      X_train["vcat"] = X_train["vA"].apply(
          lambda x: 1 if x > 0.5 else 2
      )
      X_train["vcat2"] = X_train["vB"].apply(
          lambda x: 3 if x > 0.5 else 4
      )
      y_train = y % 2
  
      model.fit(X_train, y_train)
  
      hb_model = hummingbird.ml.convert(model, "torch")
      self.assertTrue(hb_model is not None)
      np.testing.assert_allclose(
          model.predict(X_train),
          hb_model.predict(X_train),
          rtol=1e-6,
          atol=1e-6,
      )
      np.testing.assert_allclose(
          model.predict_proba(X_train),
          hb_model.predict_proba(X_train),
          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 50 for calling the TestSklearnLinearClassifiers obj's underlying member method code with example usage: obj.test_logistic_regression_pandas() without return types