code: def test_nmf_float32_float64_consistency(Estimator, solver):
      # Check that the result of NMF is the same between float32 and float64
      X = np.random.RandomState(0).randn(50, 7)
      np.abs(X, out=X)
      nmf32 = Estimator(random_state=0, tol=1e-3, **solver)
      W32 = nmf32.fit_transform(X.astype(np.float32))
      nmf64 = Estimator(random_state=0, tol=1e-3, **solver)
      W64 = nmf64.fit_transform(X)
  
      assert_allclose(W32, W64, atol=1e-5)
  
imports: import re
  import sys
  import warnings
  from io import StringIO
  import numpy
  import pytest
  from scipy import linalg
  from sklearn.base import clone
  from sklearn.decomposition import NMF, MiniBatchNMF, non_negative_factorization
  from sklearn.decomposition import _nmf
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_equal, ignore_warnings
  from sklearn.utils.extmath import squared_norm
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 29 for calling a global function with example usage: test_nmf_float32_float64_consistency(Estimator, solver) without return types