code: def test_print_forest_shape(small_classifier_and_preds):
      model_path, model_type, X, xgb_preds = small_classifier_and_preds
      m = ForestInference.load(
          model_path,
          model_type=model_type,
          output_class=True,
          compute_shape_str=True,
      )
      for substr in [
          "model size",
          " MB",
          "Depth histogram:",
          "Leaf depth",
          "Depth histogram fingerprint",
          "Avg nodes per tree",
      ]:
          assert substr in m.shape_str
  
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 13 for calling a global function with example usage: test_print_forest_shape(small_classifier_and_preds) without return types