code: def test_fit_gibbs(csc_container):
      # XXX: this test is very seed-dependent! It probably needs to be rewritten.
  
      # Gibbs on the RBM hidden layer should be able to recreate [[0], [1]]
      # from the same input
      rng = np.random.RandomState(42)
      X = np.array([[0.0], [1.0]])
      rbm1 = BernoulliRBM(
          n_components=2, batch_size=2, n_iter=42, random_state=rng
      )
      # you need that much iters
      rbm1.fit(X)
      assert_almost_equal(
          rbm1.components_,
          np.array([[0.02649814], [0.02009084]]),
          decimal=4,
      )
      assert_almost_equal(rbm1.gibbs(X), X)
  
      # Gibbs on the RBM hidden layer should be able to recreate [[0], [1]] from
      # the same input even when the input is sparse, and test against non-sparse
      rng = np.random.RandomState(42)
      X = csc_container([[0.0], [1.0]])
      rbm2 = BernoulliRBM(
          n_components=2, batch_size=2, n_iter=42, random_state=rng
      )
      rbm2.fit(X)
      assert_almost_equal(
          rbm2.components_,
          np.array([[0.02649814], [0.02009084]]),
          decimal=4,
      )
      assert_almost_equal(rbm2.gibbs(X), X.toarray())
      assert_almost_equal(rbm1.components_, rbm2.components_)
  
imports: import re
  import sys
  from io import StringIO
  import numpy
  import pytest
  from sklearn.datasets import load_digits
  from sklearn.neural_network import BernoulliRBM
  from sklearn.utils._testing import assert_allclose, assert_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS, LIL_CONTAINERS
  from sklearn.utils.validation import assert_all_finite
label_desc: Use image node_id 7 for calling a global function with example usage: test_fit_gibbs(csc_container) without return types