code: def plot_matrices(cov, prec, title, labels):
      """Plot covariance and precision matrices, for a given processing."""
      prec = prec.copy()  # avoid side effects
  
      # Put zeros on the diagonal, for graph clarity.
      size = prec.shape[0]
      prec[list(range(size)), list(range(size))] = 0
      span = max(abs(prec.min()), abs(prec.max()))
  
      # Display covariance matrix
      plotting.plot_matrix(
          cov,
          cmap=plotting.cm.bwr,
          vmin=-1,
          vmax=1,
          title=f"{title} / covariance",
          labels=labels,
      )
      # Display precision matrix
      plotting.plot_matrix(
          prec,
          cmap=plotting.cm.bwr,
          vmin=-span,
          vmax=span,
          title=f"{title} / precision",
          labels=labels,
      )
  
imports: import numpy
  from nilearn import plotting
  from nilearn import datasets
  from nilearn.maskers import NiftiMapsMasker
  from nilearn.connectome import GroupSparseCovarianceCV
label_desc: Use image node_id 1 for calling a global function with example usage: plot_matrices(cov, prec, title, labels) without return types