code: def test_verify_estimator_class(self):
      np.random.seed(8)
      (
          performance_dict,
          hyperparameters,
      ) = functions.verify_estimator_class(
          RandomForestClassifier(),
          "predict_proba",
          dict(Accuracy=self.source),
          self.dataset_properties,
      )
      assert round(performance_dict["Accuracy"], 3) == 0.8
      assert hyperparameters == {
          "warm_start": False,
          "oob_score": False,
          "n_jobs": 1,
          "verbose": 0,
          "max_leaf_nodes": None,
          "bootstrap": True,
          "min_samples_leaf": 1,
          "n_estimators": 10,
          "min_samples_split": 2,
          "min_weight_fraction_leaf": 0.0,
          "criterion": "gini",
          "random_state": None,
          "min_impurity_split": None,
          "min_impurity_decrease": 0.0,
          "max_features": "auto",
          "max_depth": None,
          "class_weight": None,
      }
  
imports: from __future__ import absolute_import, print_function, division, unicode_literals
  import unittest
  import os
  import numpy
  from xcessiv import functions, exceptions
  from sklearn.datasets import load_digits
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.decomposition import PCA
  from sklearn.pipeline import Pipeline
  import pickle
label_desc: Use image node_id 2 for calling the TestVerifyEstimatorClass obj's underlying member method code with example usage: obj.test_verify_estimator_class() without return types