code: def test_1():
      (
          train_inputs,
          train_targets,
          holdout_inputs,
          holdout_targets,
      ) = get_pima_data()
  
      feature_engineer = FeatureEngineer()
      feature_engineer.add_step(set_nan_0)
      assert feature_engineer._steps[-1].name == "set_nan_0"
      feature_engineer.add_step(impute_negative_one_0)
      assert feature_engineer._steps[-1].name == "impute_negative_one_0"
      feature_engineer(
          "pre_cv",
          train_inputs=train_inputs.copy(),
          holdout_inputs=holdout_inputs.copy(),
      )
  
      expected_train_inputs = [
          [1, 85, 66, 29, -1, 26.6, 0.351, 31],
          [8, 183, 64, -1, -1, 23.3, 0.672, 32],
          [1, 89, 66, 23, 94, 28.1, 0.167, 21],
          [0, 137, 40, 35, 168, 43.1, 2.288, 33],
      ]
      expected_holdout_inputs = [[6, 148, 72, 35, -1, 33.6, 0.627, 50]]
  
      assert_array_almost_equal(
          feature_engineer.datasets["train_inputs"],
          expected_train_inputs,
      )
      assert_array_almost_equal(
          feature_engineer.datasets["holdout_inputs"],
          expected_holdout_inputs,
      )
  
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  from hyperparameter_hunter.callbacks.recipes import dataset_recorder
  import numpy
  from numpy.testing import assert_array_almost_equal
  import pandas
  from pandas.testing import assert_frame_equal
  import pytest
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 9 for calling a global function with example usage: test_1() without return types