code: def test_block_covariances_est(estimator, rndstate):
      """Test block covariance for multiple estimators"""
      n_matrices, n_channels, n_times = 2, 12, 100
      x = rndstate.randn(n_matrices, n_channels, n_times)
  
      if estimator is None:
          cov = block_covariances(x, [4, 4, 4])
          assert cov.shape == (n_matrices, n_channels, n_channels)
      elif estimator == "truc":
          with pytest.raises(ValueError):
              block_covariances(x, [4, 4, 4], estimator=estimator)
      else:
          cov = block_covariances(x, [4, 4, 4], estimator=estimator)
          assert cov.shape == (n_matrices, n_channels, n_channels)
  
imports: from numpy.testing import assert_array_almost_equal
  import numpy
  from scipy.linalg import block_diag
  from scipy.signal import welch, csd, coherence
  from sklearn.covariance import empirical_covariance
  import pytest
  from pyriemann.utils.covariance import covariances, covariances_EP, covariances_X, eegtocov, cross_spectrum, cospectrum, coherence, normalize, get_nondiag_weight, block_covariances, _complex_estimator
  from pyriemann.utils.test import is_real, is_hermitian, is_sym_pos_def, is_herm_pos_def
label_desc: Use image node_id 8 for calling a global function with example usage: test_block_covariances_est(estimator, rndstate) without return types