code: def test_pca_zero_noise_variance_edge_cases():
      # ensure that noise_variance_ is 0 in edge cases
      # when n_components == min(n_samples, n_features)
      n, p = 100, 3
  
      rng = np.random.RandomState(0)
      X = rng.randn(n, p) * 0.1 + np.array([3, 4, 5])
      dX = da.from_array(X, chunks=(n, p))
      # arpack raises ValueError for n_components == min(n_samples,
      # n_features)
      svd_solvers = ["full", "randomized"]
  
      for svd_solver in svd_solvers:
          pca = dd.PCA(svd_solver=svd_solver, n_components=p)
          pca.fit(dX)
          assert pca.noise_variance_ == 0
  
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 25 for calling a global function with example usage: test_pca_zero_noise_variance_edge_cases() without return types