code: def _count_support_diff(m, m_hat):
      n_features, _ = m.shape
  
      m_no_diag = m.copy()
      m_no_diag[np.diag_indices(n_features)] = 0
      m_hat_no_diag = m_hat.copy()
      m_hat_no_diag[np.diag_indices(n_features)] = 0
  
      m_nnz = len(np.nonzero(m_no_diag.flat)[0])
      m_hat_nnz = len(np.nonzero(m_hat_no_diag.flat)[0])
  
      nnz_intersect = len(
          np.intersect1d(
              np.nonzero(m_no_diag.flat)[0],
              np.nonzero(m_hat_no_diag.flat)[0],
          )
      )
      return m_nnz + m_hat_nnz - (2 * nnz_intersect)
  
imports: import sys
  import numpy
  import tabulate
  import time
  from sklearn.model_selection import GridSearchCV
  from sklearn.datasets import make_sparse_spd_matrix
  from sklearn.covariance import GraphicalLassoCV, ledoit_wolf
  import matplotlib.pyplot
  from inverse_covariance import QuicGraphicalLasso, QuicGraphicalLassoCV, QuicGraphicalLassoEBIC, AdaptiveGraphicalLasso, ModelAverage
label_desc: Use image node_id 15 for calling a global function with example usage: _count_support_diff(m, m_hat) and returns: unknown