code: def compute_region_stat(
      Tvalues, labels, label_values, random_Tvalues
  ):
      """
      Fisher_values = compute_region_stat(Tvalues, labels, label_values, random_Tvalues)
      Compute summary statistics in each cluster
      In:  see permutation_test_onesample class docstring
      Out: Fisher_values Array of size nregions
      """
      Fisher_values = np.zeros(len(label_values), float)
      pseudo_p_values = 1 - np.searchsorted(
          random_Tvalues, Tvalues
      ) / float(len(random_Tvalues))
      for i in range(len(label_values)):
          I = np.where(labels == label_values[i])[0]
          Fisher_values[i] = -np.sum(np.log(pseudo_p_values[I]))
      return 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 10 for calling a global function with example usage: compute_region_stat(Tvalues, labels, label_values, random_Tvalues) and returns: Fisher_values