code: def small_classifier_and_preds(tmpdir_factory, request):
      X, y = simulate_data(
          500, 10, random_state=43210, classification=True
      )
  
      ext = "json" if request.param == "json" else "model"
      model_type = (
          "xgboost_json" if request.param == "json" else "xgboost"
      )
      model_path = str(
          tmpdir_factory.mktemp("models").join(f"small_class.{ext}")
      )
      bst = _build_and_save_xgboost(model_path, X, y)
      # just do within-sample since it's not an accuracy test
      dtrain = xgb.DMatrix(X, label=y)
      xgb_preds = bst.predict(dtrain)
  
      return (model_path, model_type, X, xgb_preds)
  
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 7 for calling a global function with example usage: small_classifier_and_preds(tmpdir_factory, request) and returns: model_path,  model_type,  X,  xgb_preds