code: def compute_cluster_stats(
      Tvalues, labels, random_Tvalues, cluster_stats=["size", "Fisher"]
  ):
      """
      size_values, Fisher_values = compute_cluster_stats(Tvalues, labels, random_Tvalues, cluster_stats=["size","Fisher"])
      Compute summary statistics in each cluster
      In:  see permutation_test_onesample class docstring
      Out: size_values   Array of size nclust, or None if "size" not in cluster_stats
           Fisher_values Array of size nclust, or None if "Fisher" not in cluster_stats
      """
      nclust = max(labels) + 1
      if nclust == 0:
          if "size" in cluster_stats:
              size_values = np.array([0])
          else:
              size_values = None
          if "Fisher" in cluster_stats:
              Fisher_values = np.array([0])
          else:
              Fisher_values = None
      else:
          if "size" in cluster_stats:
              size_values = np.zeros(nclust, int)
          else:
              size_values = None
          if "Fisher" in cluster_stats:
              Fisher_values = np.zeros(nclust, float)
              ndraws = len(random_Tvalues)
              pseudo_p_values = 1 - np.searchsorted(
                  random_Tvalues, Tvalues
              ) / float(ndraws)
          else:
              Fisher_values = None
      for i in range(nclust):
          I = np.where(labels == i)[0]
          if "size" in cluster_stats:
              size_values[i] = len(I)
          if "Fisher" in cluster_stats:
              Fisher_values[i] = -np.sum(np.log(pseudo_p_values[I]))
      return size_values, Fisher_values
  
imports: import numpy
  import warnings
  from nipy.algorithms.graph import wgraph_from_3d_grid
  from nipy.algorithms.graph.field import Field, field_from_graph_and_data
  from ..utils import zscore
  from .onesample import stat
  from .onesample import stat_mfx
  from .twosample import stat
  from .twosample import stat_mfx
label_desc: Use image node_id 9 for calling a global function with example usage: compute_cluster_stats(Tvalues, labels, random_Tvalues, cluster_stats) and returns: size_values,  Fisher_values