code: def test_fit_ml_model_with_evaluation_with_user_provided_bounds():
      """Tests fit_ml_model_with_evaluation
      with min_admissible_value and max_admissible_value"""
      data = generate_test_data_for_fitting()
      df = data["df"]
      model_formula_str = data["model_formula_str"]
      y_test = data["y_test"]
      df_test = data["df_test"]
      y_col = "y"
  
      trained_model = fit_ml_model_with_evaluation(
          df=df,
          model_formula_str=model_formula_str,
          min_admissible_value=-7,
          max_admissible_value=20.00,
      )
  
      pred_res = predict_ml(fut_df=df_test, trained_model=trained_model)
      fut_df = pred_res["fut_df"]
      y_test_pred = fut_df[y_col]
  
      err = calc_pred_err(y_test, y_test_pred)
      enum = EvaluationMetricEnum.MeanAbsoluteError
      assert err[enum.get_metric_name()] < 3.0
      enum = EvaluationMetricEnum.RootMeanSquaredError
      assert err[enum.get_metric_name()] < 3.0
      enum = EvaluationMetricEnum.Correlation
      assert err[enum.get_metric_name()] > 0.5
  
      # testing actual values on a smaller set
      pred_res = predict_ml(
          fut_df=df_test[:10], trained_model=trained_model
      )
      fut_df = pred_res["fut_df"]
      y_test_pred = fut_df[y_col]
  
      expected_values = [
          8.36,
          11.19,
          1.85,
          15.57,
          16.84,
          14.44,
          20.00,
          9.02,
          1.81,
          -7.00,
      ]
      assert list(y_test_pred.round(2)) == expected_values
  
imports: import warnings
  import numpy
  import pandas
  import pytest
  import scipy
  from pandas.testing import assert_frame_equal
  from patsy import dmatrices
  from greykite.algo.common.ml_models import breakdown_regression_based_prediction
  from greykite.algo.common.ml_models import design_mat_from_formula
  from greykite.algo.common.ml_models import fit_ml_model
  from greykite.algo.common.ml_models import fit_ml_model_with_evaluation
  from greykite.algo.common.ml_models import fit_model_via_design_matrix
  from greykite.algo.common.ml_models import get_h_mat
  from greykite.algo.common.ml_models import get_intercept_col_from_design_mat
  from greykite.algo.common.ml_models import predict_ml
  from greykite.algo.common.ml_models import predict_ml_with_uncertainty
  from greykite.algo.uncertainty.conditional.conf_interval import predict_ci
  from greykite.common.constants import ERR_STD_COL
  from greykite.common.constants import QUANTILE_SUMMARY_COL
  from greykite.common.constants import TimeFeaturesEnum
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.evaluation import calc_pred_err
  from greykite.common.features.timeseries_features import build_time_features_df
  from greykite.common.features.timeseries_features import fourier_series_multi_fcn
  from greykite.common.features.timeseries_features import get_fourier_col_name
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import gen_sliced_df
label_desc: Use image node_id 23 for calling a global function with example usage: test_fit_ml_model_with_evaluation_with_user_provided_bounds() without return types