code: def contingency_table(
      im_true, im_test, *, ignore_labels=None, normalize=False
  ):
      """
      Return the contingency table for all regions in matched segmentations.
  
      Parameters
      ----------
      im_true : ndarray of int
          Ground-truth label image, same shape as im_test.
      im_test : ndarray of int
          Test image.
      ignore_labels : sequence of int, optional
          Labels to ignore. Any part of the true image labeled with any of these
          values will not be counted in the score.
      normalize : bool
          Determines if the contingency table is normalized by pixel count.
  
      Returns
      -------
      cont : scipy.sparse.csr_matrix
          A contingency table. `cont[i, j]` will equal the number of voxels
          labeled `i` in `im_true` and `j` in `im_test`.
      """
  
      if ignore_labels is None:
          ignore_labels = []
      im_test_r = im_test.reshape(-1)
      im_true_r = im_true.reshape(-1)
      data = np.isin(im_true_r, ignore_labels, invert=True).astype(
          float
      )
      if normalize:
          data /= np.count_nonzero(data)
      cont = sparse.coo_matrix((data, (im_true_r, im_test_r))).tocsr()
      return cont
  
label_desc: Use image node_id 1 for calling a global function with example usage: contingency_table(im_true, im_test) and returns: cont