code: def test_incremental_pca_partial_fit_float_division():
      # Test to ensure float division is used in all versions of Python
      # (non-regression test for issue #9489)
  
      rng = np.random.RandomState(0)
      A = rng.randn(5, 3) + 2
      B = rng.randn(7, 3) + 5
      A = da.from_array(A, chunks=[3, -1])
      B = da.from_array(B, chunks=[3, -1])
  
      pca = IncrementalPCA(n_components=2)
      pca.partial_fit(A)
      # Set n_samples_seen_ to be a floating point number instead of an int
      pca.n_samples_seen_ = float(pca.n_samples_seen_)
      pca.partial_fit(B)
      singular_vals_float_samples_seen = pca.singular_values_
  
      pca2 = IncrementalPCA(n_components=2)
      pca2.partial_fit(A)
      pca2.partial_fit(B)
      singular_vals_int_samples_seen = pca2.singular_values_
  
      np.testing.assert_allclose(
          singular_vals_float_samples_seen,
          singular_vals_int_samples_seen,
      )
  
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 18 for calling a global function with example usage: test_incremental_pca_partial_fit_float_division() without return types