code: def test_pca_inverse():
      # Test that the projection of data can be inverted
      rng = np.random.RandomState(0)
      n, p = 50, 3
      X = rng.randn(n, p)  # spherical data
      X[:, 1] *= 0.00001  # make middle component relatively small
      X += [5, 4, 3]  # make a large mean
      dX = da.from_array(X, chunks=(n // 2, p))
  
      # same check that we can find the original data from the transformed
      # signal (since the data is almost of rank n_components)
      pca = dd.PCA(n_components=2, svd_solver="full").fit(dX)
      Y = pca.transform(dX)
      Y_inverse = pca.inverse_transform(Y)
      assert_almost_equal(X, Y_inverse, decimal=3)
  
      # same as above with whitening (approximate reconstruction)
      for solver in solver_list:
          pca = dd.PCA(n_components=2, whiten=True, svd_solver=solver)
          pca.fit(dX)
          Y = pca.transform(dX)
          Y_inverse = pca.inverse_transform(Y)
          assert_eq(dX, Y_inverse, atol=1e-3)
  
imports: from itertools import product
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import scipy
  import sklearn.decomposition
  from dask.array.utils import assert_eq
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_raises
  from sklearn import datasets
  from sklearn.utils import check_random_state
  import dask_ml.decomposition
  from dask_ml._compat import DASK_2_26_0
  from dask_ml.utils import assert_estimator_equal, flip_vector_signs, svd_flip
label_desc: Use image node_id 10 for calling a global function with example usage: test_pca_inverse() without return types