code: def _pare_subpeaks(xyz, ijk, vals, min_distance):
      """Reduce list of subpeaks based on distance.
  
      Parameters
      ----------
      xyz : 2D numpy.ndarray
          Subpeak coordinates to reduce. Rows correspond to peaks, columns
          correspond to x, y, and z dimensions.
      ijk : 2D numpy.ndarray
          The subpeak coordinates in ``xyz``, but converted to matrix indices.
      vals : 1D numpy.ndarray
          The statistical value associated with each subpeak in ``xyz``/``ijk``.
      min_distance : float
          The minimum distance between subpeaks, in millimeters.
  
      Returns
      -------
      ijk : 2D numpy.ndarray
          The reduced index of subpeaks.
      vals : 1D numpy.ndarray
          The statistical values associated with the reduced set of subpeaks.
      """
      keep_idx = np.ones(xyz.shape[0]).astype(bool)
      for i in range(xyz.shape[0]):
          for j in range(i + 1, xyz.shape[0]):
              if keep_idx[i] == 1:
                  dist = np.linalg.norm(xyz[i, :] - xyz[j, :])
                  keep_idx[j] = dist > min_distance
      ijk = ijk[keep_idx, :]
      vals = vals[keep_idx]
      return ijk, vals
  
file_path: nilearn/nilearn/reporting/get_clusters_table.py
imports: import warnings
  from string import ascii_lowercase
  import nibabel
  import numpy
  import pandas
  from scipy.ndimage import center_of_mass, generate_binary_structure, label, maximum_filter, minimum_filter
  from nilearn._utils import check_niimg_3d
  from nilearn._utils.niimg import safe_get_data
  from nilearn.image import new_img_like, threshold_img
  from nilearn.image.resampling import coord_transform
label_desc: Use image node_id 5 for calling a global function with example usage: _pare_subpeaks(xyz, ijk, vals, min_distance) and returns: ijk,  vals