code: def test_lda_transform():
      # Test LDA transform.
      # Transform result cannot be negative and should be normalized
      rng = np.random.RandomState(0)
      X = rng.randint(5, size=(20, 10))
      n_components = 3
      lda = LatentDirichletAllocation(
          n_components=n_components, random_state=rng
      )
      X_trans = lda.fit_transform(X)
      assert (X_trans > 0.0).any()
      assert_array_almost_equal(
          np.sum(X_trans, axis=1), np.ones(X_trans.shape[0])
      )
  
imports: import sys
  from io import StringIO
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from scipy.linalg import block_diag
  from scipy.special import psi
  from sklearn.decomposition import LatentDirichletAllocation
  from sklearn.decomposition._online_lda_fast import _dirichlet_expectation_1d, _dirichlet_expectation_2d
  from sklearn.exceptions import NotFittedError
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_almost_equal, if_safe_multiprocessing_with_blas
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 7 for calling a global function with example usage: test_lda_transform() without return types