code: def check_pca_int_dtype_upcast_to_double(svd_solver):
      # Ensure that all int types will be upcast to float64
      X_i64 = np.random.RandomState(0).randint(0, 1000, (1000, 4))
      X_i64 = X_i64.astype(np.int64, copy=False)
      X_i32 = X_i64.astype(np.int32, copy=False)
  
      pca_64 = PCA(
          n_components=3, svd_solver=svd_solver, random_state=0
      ).fit(X_i64)
      pca_32 = PCA(
          n_components=3, svd_solver=svd_solver, random_state=0
      ).fit(X_i32)
  
      assert pca_64.components_.dtype == np.float64
      assert pca_32.components_.dtype == np.float64
      assert pca_64.transform(X_i64).dtype == np.float64
      assert pca_32.transform(X_i32).dtype == np.float64
  
      assert_allclose(pca_64.components_, pca_32.components_, rtol=1e-4)
  
imports: import re
  import warnings
  import numpy
  import pytest
  import scipy
  from numpy.testing import assert_array_equal
  from sklearn import config_context, datasets
  from sklearn.base import clone
  from sklearn.datasets import load_iris, make_classification
  from sklearn.decomposition import PCA
  from sklearn.decomposition._pca import _assess_dimension, _infer_dimension
  from sklearn.utils._array_api import _atol_for_type, _convert_to_numpy, yield_namespace_device_dtype_combinations
  from sklearn.utils._array_api import device
  from sklearn.utils._testing import _array_api_for_tests, assert_allclose
  from sklearn.utils.estimator_checks import _get_check_estimator_ids, check_array_api_input_and_values
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 33 for calling a global function with example usage: check_pca_int_dtype_upcast_to_double(svd_solver) without return types