code: def test_run_xgb_pandas(self):
      cali = fetch_california_housing()
      data = pd.DataFrame(cali.data)
      data.columns = cali.feature_names
  
      X, y = data.iloc[:, :-1], data.iloc[:, -1]
      # Split the data into training and testing dataset by taking train_size as 75%
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=0.75, random_state=42
      )
  
      model = xgb.XGBRegressor(
          colsample_bytree=0.3,
          learning_rate=0.1,
          max_depth=5,
          alpha=10,
          n_estimators=10,
      )
      model.fit(X_train, y_train)
  
      torch_model = hummingbird.ml.convert(model, "torch")
  
      self.assertIsNotNone(torch_model)
      np.testing.assert_allclose(
          model.predict(X_test),
          torch_model.predict(X_test),
          rtol=1e-06,
          atol=1e-06,
      )
  
imports: import unittest
  import warnings
  import numpy
  from sklearn.datasets import fetch_california_housing
  from sklearn.model_selection import train_test_split
  import hummingbird.ml
  from hummingbird.ml._utils import xgboost_installed, tvm_installed, pandas_installed
  from hummingbird.ml import constants
  from tree_utils import gbdt_implementation_map
label_desc: Use image node_id 25 for calling the TestXGBoostConverter obj's underlying member method code with example usage: obj.test_run_xgb_pandas() without return types