code: def test_anomaly(self, mock_handler):
      df = pd.DataFrame(
          {
              "Col1": [
                  0.263995357,
                  0.764928588,
                  0.13842355,
                  0.935242061,
                  0.605866573,
                  0.518789697,
                  0.912225161,
                  0.608234451,
                  0.723781923,
                  0.73359095,
              ],
              "Col2": [
                  0.546092303,
                  0.65397459,
                  0.065575135,
                  0.227771913,
                  0.845269445,
                  0.837065879,
                  0.272378939,
                  0.331678698,
                  0.429296975,
                  0.367422001,
              ],
              "Col3": [
                  0.336714104,
                  0.538842451,
                  0.192801069,
                  0.553562822,
                  0.074514511,
                  0.332993162,
                  0.365792205,
                  0.861309323,
                  0.899016587,
                  0.088600152,
              ],
              "Col4": [
                  0.092107835,
                  0.995016662,
                  0.014465045,
                  0.176370646,
                  0.241530075,
                  0.514723634,
                  0.562208164,
                  0.158963258,
                  0.073715215,
                  0.208463224,
              ],
              "Col5": [
                  0.325261175,
                  0.805967636,
                  0.957033424,
                  0.331664957,
                  0.307923366,
                  0.355314772,
                  0.50189852,
                  0.558449452,
                  0.885169295,
                  0.182754409,
              ],
              "Col6": [
                  0.212464853,
                  0.780304761,
                  0.458443656,
                  0.634508561,
                  0.373030452,
                  0.465650668,
                  0.413997158,
                  0.013080054,
                  0.570250227,
                  0.736672363,
              ],
              "Col7": [
                  0.258565714,
                  0.437317789,
                  0.559647989,
                  0.109202597,
                  0.994553306,
                  0.896994183,
                  0.488468506,
                  0.251942977,
                  0.017265143,
                  0.538513303,
              ],
              "Col8": [
                  0.869236755,
                  0.277978893,
                  0.42307639,
                  0.11247202,
                  0.183727053,
                  0.034959735,
                  0.111113968,
                  0.249329646,
                  0.550683376,
                  0.049843054,
              ],
              "Col9": [
                  0.197077957,
                  0.843918225,
                  0.24339588,
                  0.281278233,
                  0.329148141,
                  0.73458152,
                  0.191947043,
                  0.927804425,
                  0.71326865,
                  0.891548497,
              ],
              "Col10": [
                  0.292984504,
                  0.70343162,
                  0.43962138,
                  0.107867968,
                  0.922947409,
                  0.25345779,
                  0.29565178,
                  0.355286799,
                  0.980911322,
                  0.308864217,
              ],
          }
      )
  
      self.set_handler(mock_handler, name="pg", tables={"anomaly": df})
  
      # create project
      self.run_sql("create database proj;")
  
      # create predictor
      self.run_sql(
          """
              CREATE MODEL proj.my_pycaret_anom_model
              FROM pg
                  (SELECT Col1, Col2, Col3, Col4, Col5, Col6, Col7, Col8, Col9, Col10 FROM anomaly)
              PREDICT Col10
              USING
                engine = 'pycaret',
                model_type = 'anomaly',
                model_name = 'iforest',
                setup_session_id = 123,
                setup_fold = 2;
          """
      )
      self.wait_predictor("proj", "my_pycaret_anom_model")
  
      # run predict
      # TODO: is there a workaround for this? (it works when ran in web UI)
      ret = self.run_sql(
          """
              SELECT m.Anomaly
              FROM pg.anomaly as t
              JOIN proj.my_pycaret_anom_model AS m;
          """
      )
  
      assert int(ret["Anomaly"].iloc[0]) == 0
  
file_path: mindsdb/mindsdb/integrations/handlers/pycaret_handler/test/test_pycaret.py
imports: import importlib
  import time
  from unittest.mock import patch
  import pandas
  import pytest
  from mindsdb_sql import parse_sql
  from tests.unit.executor_test_base import BaseExecutorTest
label_desc: Use image node_id 5 for calling the TestPyCaret obj's underlying member method code with example usage: obj.test_anomaly(mock_handler) without return types