code: def test_xgb_autolog_with_sklearn_outputs_do_not_reflect_training_dataset_mutations():
      original_xgb_regressor_fit = xgb.XGBRegressor.fit
      original_xgb_regressor_predict = xgb.XGBRegressor.predict
  
      def patched_xgb_regressor_fit(self, *args, **kwargs):
          X = args[0]
          X["TESTCOL"] = 5
          return original_xgb_regressor_fit(self, *args, **kwargs)
  
      def patched_xgb_regressor_predict(self, *args, **kwargs):
          X = args[0]
          X["TESTCOL"] = 5
          return original_xgb_regressor_predict(self, *args, **kwargs)
  
      with mock.patch(
          "xgboost.XGBRegressor.fit", patched_xgb_regressor_fit
      ), mock.patch(
          "xgboost.XGBRegressor.predict", patched_xgb_regressor_predict
      ):
          xgb.XGBRegressor.fit = patched_xgb_regressor_fit
          xgb.XGBRegressor.predict = patched_xgb_regressor_predict
  
          mlflow.xgboost.autolog(
              log_models=True,
              log_model_signatures=True,
              log_input_examples=True,
          )
  
          X = pd.DataFrame(
              {
                  "Total Volume": [64236.62, 54876.98, 118220.22],
                  "Total Bags": [8696.87, 9505.56, 8145.35],
                  "Small Bags": [8603.62, 9408.07, 8042.21],
                  "Large Bags": [93.25, 97.49, 103.14],
                  "XLarge Bags": [0.0, 0.0, 0.0],
              }
          )
          y = pd.Series([1.33, 1.35, 0.93])
  
          params = {"n_estimators": 10, "reg_lambda": 1}
          model = xgb.XGBRegressor(**params)
          model.fit(X, y)
  
          run_artifact_uri = mlflow.last_active_run().info.artifact_uri
          model_conf = get_model_conf(run_artifact_uri)
          input_example = pd.read_json(
              os.path.join(
                  run_artifact_uri, "model", "input_example.json"
              ),
              orient="split",
          )
          model_signature_input_names = [
              inp.name for inp in model_conf.signature.inputs.inputs
          ]
          assert "XLarge Bags" in model_signature_input_names
          assert "XLarge Bags" in input_example.columns
          assert "TESTCOL" not in model_signature_input_names
          assert "TESTCOL" not in input_example.columns
  
imports: import functools
  import json
  import os
  import pickle
  from unittest import mock
  import matplotlib
  import numpy
  import pandas
  import pytest
  import xgboost
  import yaml
  from packaging.version import Version
  from sklearn import datasets
  import mlflow
  import mlflow.xgboost
  from mlflow import MlflowClient
  from mlflow.models import Model
  from mlflow.models.utils import _read_example
  from mlflow.types.utils import _infer_schema
  from mlflow.utils.autologging_utils import BatchMetricsLogger, picklable_exception_safe_function
  from mlflow.xgboost._autolog import IS_TRAINING_CALLBACK_SUPPORTED, autolog_callback
label_desc: Use image node_id 14 for calling a global function with example usage: test_xgb_autolog_with_sklearn_outputs_do_not_reflect_training_dataset_mutations() and returns: original_xgb_regressor_fit, original_xgb_regressor_predict