code: def test_get_pipeline(df):
      mt = MyTemplate()
      # Initializes attributes needed by the function
      mt.regressor_cols = mt.get_regressor_cols()
      mt.lagged_regressor_cols = mt.get_lagged_regressor_info()[
          "lagged_regressor_cols"
      ]
      metric = EvaluationMetricEnum.MeanSquaredError
      mt.score_func = metric.name
      mt.score_func_greater_is_better = (
          metric.get_metric_greater_is_better()
      )
      mt.config = ForecastConfig(
          coverage=0.9,
          evaluation_metric_param=EvaluationMetricParam(
              cv_selection_metric=metric.name
          ),
      )
      # Checks get_pipeline output
      pipeline = mt.get_pipeline()
      assert isinstance(pipeline, sklearn.pipeline.Pipeline)
      estimator = pipeline.steps[-1][-1]
      assert isinstance(estimator, SilverkiteEstimator)
      assert estimator.coverage == mt.config.coverage
      assert mt.estimator is not estimator
      assert mt.estimator.coverage is None
      expected_col_names = [
          "regressor1",
          "regressor2",
          "regressor_categ",
          "regressor_bool",
      ]
      assert (
          pipeline.named_steps["input"]
          .transformer_list[2][1]
          .named_steps["select_reg"]
          .column_names
          == expected_col_names
      )
      assert_eval_function_equal(
          pipeline.steps[-1][-1].score_func, metric.get_metric_func()
      )
  
imports: import dataclasses
  import datetime
  import pytest
  import sklearn
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.enums import TimeEnum
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.python_utils import assert_equal
  from greykite.common.python_utils import unique_elements_in_list
  from greykite.common.testing_utils import assert_eval_function_equal
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.framework.templates.autogen.forecast_config import EvaluationMetricParam
  from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
  from greykite.framework.templates.autogen.forecast_config import ForecastConfig
  from greykite.framework.templates.autogen.forecast_config import MetadataParam
  from greykite.framework.templates.base_template import BaseTemplate
  from greykite.sklearn.estimator.silverkite_estimator import SilverkiteEstimator
label_desc: Use image node_id 5 for calling a global function with example usage: test_get_pipeline(df) without return types