code: def test_covariances_complex(estimator, rndstate):
      """Test covariance for complex inputs"""
      n_matrices, n_channels, n_times = 3, 4, 50
      x = rndstate.randn(
          n_matrices, n_channels, n_times
      ) + 1j * rndstate.randn(n_matrices, n_channels, n_times)
  
      cov = covariances(x, estimator=estimator)
      assert cov.shape == (n_matrices, n_channels, n_channels)
      assert is_herm_pos_def(cov)
  
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 4 for calling a global function with example usage: test_covariances_complex(estimator, rndstate) without return types