code: def test_incremental_pca_batch_values():
      # Test that components_ values are stable over batch sizes.
      rng = np.random.RandomState(1999)
      n_samples = 100
      n_features = 3
      X = rng.randn(n_samples, n_features)
      X = da.from_array(X, chunks=[40, -1])
      all_components = []
      batch_sizes = np.arange(20, 40, 3)
      for batch_size in batch_sizes:
          ipca = IncrementalPCA(
              n_components=None, batch_size=batch_size
          ).fit(X)
          all_components.append(ipca.components_)
  
      for i, j in zip(all_components[:-1], all_components[1:]):
          assert_almost_equal(i, j, decimal=1)
  
imports: import numpy
  import pytest
  from dask import array
  from sklearn import datasets, decomposition
  from sklearn.decomposition import PCA
  from dask_ml.decomposition import IncrementalPCA
  from dask_ml.utils import flip_vector_signs
label_desc: Use image node_id 10 for calling a global function with example usage: test_incremental_pca_batch_values() without return types