code: def multiplot(named_mats, suptitle):
      num_rows = len(named_mats) / 3
      num_plots = int(np.ceil(num_rows / 4.0))
      for nn in range(num_plots):
          plt.figure(figsize=(10, 8))
          plt.subplots_adjust(left=0.02, right=0.98, hspace=0.4)
          for i, item in enumerate(
              named_mats[nn * 4 * 3 : (nn + 1) * 4 * 3]
          ):
              lam = None
              if len(item) == 3:
                  name, this_mat, lam = item
              elif len(item) == 2:
                  name, this_mat = item
  
              vmax = np.abs(this_mat).max()
              ax = plt.subplot(4, 3, i + 1)
              plt.imshow(
                  np.ma.masked_values(this_mat, 0),
                  interpolation="nearest",
                  vmin=-vmax,
                  vmax=vmax,
                  cmap=plt.cm.RdBu_r,
              )
              plt.xticks(())
              plt.yticks(())
              if lam is None or lam == "":
                  plt.title("{}".format(name))
              else:
                  plt.title("{}\n(lam={:.2f})".format(name, lam))
  
          plt.suptitle(suptitle + " (page {})".format(nn), fontsize=14)
          plt.show()
  
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 3 for calling a global function with example usage: multiplot(named_mats, suptitle) without return types