code: def test_pca_check_projection_list(svd_solver):
      # Test that the projection of data is correct
      X = [[1.0, 0.0], [0.0, 1.0]]
      pca = PCA(n_components=1, svd_solver=svd_solver, random_state=0)
      X_trans = pca.fit_transform(X)
      assert X_trans.shape, (2, 1)
      assert_allclose(X_trans.mean(), 0.00, atol=1e-12)
      assert_allclose(X_trans.std(), 0.71, rtol=5e-3)
  
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 13 for calling a global function with example usage: test_pca_check_projection_list(svd_solver) without return types