code: def test_random_forest_multi_random(self):
      warnings.filterwarnings("ignore")
      n_features = 28
      n_total = 100
      np.random.seed(0)
      X = np.random.rand(n_total, n_features)
      X = np.array(X, dtype=np.float32)
      y = np.random.randint(3, size=n_total)
  
      # Create the RandomForest model
      model = RandomForestClassifier(n_estimators=10)
      model.fit(X, y)
      self._test_classifier(X, model)
  
imports: import unittest
  import warnings
  import numpy
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from onnxconverter_common.data_types import FloatTensorType
  from hummingbird.ml import convert
  from hummingbird.ml._utils import onnx_ml_tools_installed, onnx_runtime_installed, xgboost_installed
label_desc: Use image node_id 16 for calling the TestONNXDecisionTreeConverter obj's underlying member method code with example usage: obj.test_random_forest_multi_random() without return types