code: def test_pca_deterministic_output(setup):
      rng = np.random.RandomState(0)
      X = mt.tensor(rng.rand(10, 10))
  
      for solver in solver_list:
          transformed_X = np.zeros((20, 2))
          for i in range(20):
              pca = PCA(
                  n_components=2, svd_solver=solver, random_state=rng
              )
              transformed_X[i, :] = pca.fit_transform(X)[0].fetch()
          np.testing.assert_allclose(
              transformed_X,
              np.tile(transformed_X[0, :], 20).reshape(20, 2),
          )
  
imports: from itertools import product
  import numpy
  import pytest
  import scipy
  from sklearn import datasets
  from sklearn.utils._testing import assert_array_almost_equal, assert_almost_equal, assert_raises_regex, assert_raise_message, assert_raises
  from ....None import tensor
  from .._pca import PCA, _assess_dimension, _infer_dimension
label_desc: Use image node_id 30 for calling a global function with example usage: test_pca_deterministic_output(setup) without return types