code: def test_fit_with_small_max_samples(make_whas500, forest_cls):
      whas500 = make_whas500(to_numeric=True)
  
      # First fit with no restriction on max samples
      est1 = forest_cls(
          n_estimators=1, random_state=1, max_samples=None
      )
  
      # Second fit with max samples restricted to just 2
      est2 = forest_cls(n_estimators=1, random_state=1, max_samples=2)
  
      est1.fit(whas500.x, whas500.y)
      est2.fit(whas500.x, whas500.y)
  
      tree1 = est1.estimators_[0].tree_
      tree2 = est2.estimators_[0].tree_
  
      msg = "Tree without `max_samples` restriction should have more nodes"
      assert tree1.node_count > tree2.node_count, msg
  
imports: import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pytest
  from scipy import sparse
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sksurv.datasets import load_breast_cancer
  from sksurv.ensemble import ExtraSurvivalTrees, RandomSurvivalForest
  from sksurv.preprocessing import OneHotEncoder
  from sksurv.testing import assert_chf_properties, assert_cindex_almost_equal, assert_survival_function_properties
  from sksurv.tree import SurvivalTree
label_desc: Use image node_id 10 for calling a global function with example usage: test_fit_with_small_max_samples(make_whas500, forest_cls) without return types