code: def test_use_ts_predictor_with_view(self, mock_handler):
      # set integration data
  
      df = pd.DataFrame(
          [
              {"a": 1, "t": dt.datetime(2020, 1, 1), "g": "x"},
              {"a": 2, "t": dt.datetime(2020, 1, 2), "g": "x"},
              {"a": 3, "t": dt.datetime(2020, 1, 3), "g": "x"},
              {"a": 4, "t": dt.datetime(2020, 1, 1), "g": "y"},
              {"a": 5, "t": dt.datetime(2020, 1, 2), "g": "y"},
              {"a": 6, "t": dt.datetime(2020, 1, 3), "g": "y"},
              {"a": 7, "t": dt.datetime(2020, 1, 1), "g": "z"},
              {"a": 8, "t": dt.datetime(2020, 1, 2), "g": "z"},
              {"a": 9, "t": dt.datetime(2020, 1, 3), "g": "z"},
          ]
      )
      self.set_handler(mock_handler, name="pg", tables={"tasks": df})
      view_name = "vtasks"
      # --- create view ---
      self.execute(
          f"create view {view_name} (select * from pg (select * from tasks))"
      )
  
      # --- use TS predictor ---
      predicted_value = "right"
      predictor = {
          "name": "task_model",
          "predict": "p",
          "problem_definition": {
              "timeseries_settings": {
                  "is_timeseries": True,
                  "window": 10,
                  "order_by": "t",
                  "group_by": "g",
                  "horizon": 1,
              }
          },
          "dtypes": {
              "p": dtype.categorical,
              "a": dtype.integer,
              "t": dtype.date,
              "g": dtype.categorical,
          },
          "predicted_value": predicted_value,
      }
      self.set_predictor(predictor)
      ret = self.execute(
          f"""
             select task_model.*
             from mindsdb.{view_name}
             join mindsdb.task_model
             where {view_name}.t = latest
          """
      )
  
      # native query was called without filters
      assert (
          mock_handler().native_query.call_args[0][0]
          == "select * from tasks"
      )
  
      # input to predictor all 9 rows
      when_data = self.mock_predict.call_args[0][1]
      assert len(when_data) == 9
  
      # all group values in input
      group_values = {"x", "y", "z"}
      assert set(pd.DataFrame(when_data)["g"].unique()) == group_values
  
      # check prediction
      # output is has  g=='y' or None
      ret_df = self.ret_to_df(ret)
      # all group values in output
      assert set(ret_df["g"].unique()) == group_values
  
      # p is predicted value
      assert ret_df["p"][0] == predicted_value
  
imports: from unittest.mock import patch
  import datetime
  import tempfile
  import pytest
  import pandas
  import numpy
  from mindsdb_sql.render.sqlalchemy_render import SqlalchemyRender
  from mindsdb.api.executor.utilities.sql import query_df
  from mindsdb.api.mysql.mysql_proxy.utilities.lightwood_dtype import dtype
  from .executor_test_base import BaseExecutorMockPredictor
label_desc: Use image node_id 4 for calling the TestWithNativeQuery obj's underlying member method code with example usage: obj.test_use_ts_predictor_with_view(mock_handler) without return types