code: def test_pca_deterministic_output(svd_solver):
      rng = np.random.RandomState(0)
      X = rng.rand(10, 10)
  
      transformed_X = np.zeros((20, 2))
      for i in range(20):
          pca = PCA(
              n_components=2, svd_solver=svd_solver, random_state=rng
          )
          transformed_X[i, :] = pca.fit_transform(X)[0]
      assert_allclose(
          transformed_X, np.tile(transformed_X[0, :], 20).reshape(20, 2)
      )
  
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 30 for calling a global function with example usage: test_pca_deterministic_output(svd_solver) without return types