code: def test_sklearn_wrapper_update_hyperparams():
      p = SKLearnWrapper(PCA())
      p.set_hyperparams(
          HyperparameterSamples(
              {"n_components": 2, "svd_solver": "full"}
          )
      )
      p.update_hyperparams(HyperparameterSamples({"n_components": 4}))
  
      assert p.wrapped_sklearn_predictor.n_components == 4
      assert p.wrapped_sklearn_predictor.svd_solver == "full"
  
imports: import json
  import numpy
  import pytest
  from neuraxle.base import Identity
  from neuraxle.hyperparams.distributions import RandInt, Uniform
  from neuraxle.hyperparams.space import HyperparameterSamples, HyperparameterSpace
  from neuraxle.metaopt.auto_ml import AutoML, RandomSearchSampler
  from neuraxle.metaopt.callbacks import ScoringCallback
  from neuraxle.metaopt.repositories.json import HyperparamsOnDiskRepository
  from neuraxle.metaopt.validation import KFoldCrossValidationSplitter
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.data import DataShuffler
  from neuraxle.steps.flow import TrainOnlyWrapper
  from neuraxle.steps.sklearn import SKLearnWrapper
  from sklearn.decomposition import PCA
  from sklearn.ensemble import BaggingRegressor, GradientBoostingRegressor
  from sklearn.linear_model import LinearRegression, SGDClassifier, SGDRegressor
  from sklearn.metrics import median_absolute_error
label_desc: Use image node_id 8 for calling a global function with example usage: test_sklearn_wrapper_update_hyperparams() without return types