code: def test_output_args(small_classifier_and_preds):
      model_path, model_type, X, xgb_preds = small_classifier_and_preds
      fm = ForestInference.load(
          model_path,
          model_type=model_type,
          algo="TREE_REORG",
          output_class=False,
          threshold=0.50,
      )
      X = np.asarray(X)
      fil_preds = fm.predict(X)
      fil_preds = np.reshape(fil_preds, np.shape(xgb_preds))
  
      assert array_equal(fil_preds, xgb_preds, 1e-3)
  
imports: from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor
  from sklearn.datasets import make_classification, make_regression
  from cuml.internals.import_utils import has_xgboost
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml import ForestInference
  from math import ceil
  import os
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 15 for calling a global function with example usage: test_output_args(small_classifier_and_preds) without return types