code: def test_fit_dtype(toy_data, dtype, missing):
      X, y = toy_data
      if missing:
          X[:23, 0] = np.nan
      X = X.astype(dtype)
  
      tree = SurvivalTree()
      tree.fit(X, y)
      assert hasattr(tree, "tree_")
  
      pred = tree.predict(X)
      assert pred.shape[0] == X.shape[0]
  
imports: from itertools import product
  from queue import LifoQueue
  import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pandas
  import pytest
  from scipy import sparse
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.tree._tree import TREE_UNDEFINED
  from sksurv.compare import compare_survival
  from sksurv.datasets import load_breast_cancer, load_veterans_lung_cancer
  from sksurv.nonparametric import kaplan_meier_estimator, nelson_aalen_estimator
  from sksurv.tree import SurvivalTree
  from sksurv.util import Surv
label_desc: Use image node_id 13 for calling a global function with example usage: test_fit_dtype(toy_data, dtype, missing) without return types