code: def test_covariances_EP_complex(estimator, rndstate):
      """Test covariance_EP for complex input"""
      n_matrices, n_channels_x, n_channels_p, n_times = 2, 3, 3, 100
      x = rndstate.randn(
          n_matrices, n_channels_x, n_times
      ) + 1j * rndstate.randn(n_matrices, n_channels_x, n_times)
      p = rndstate.randn(n_channels_p, n_times) + 1j * rndstate.randn(
          n_channels_p, n_times
      )
      if estimator is None:
          cov = covariances_EP(x, p)
      else:
          cov = covariances_EP(x, p, estimator=estimator)
      n_dim_cov = n_channels_x + n_channels_p
      assert cov.shape == (n_matrices, n_dim_cov, n_dim_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 6 for calling a global function with example usage: test_covariances_EP_complex(estimator, rndstate) without return types