code: def _treeify(values):
          
          if len(values) == 1:  # this case causes problems later
              return values
          tree = np.empty_like(values)
          # Tree indices work as follows:
          # 0 is the root
          # 2n+1 is the left child of n
          # 2n+2 is the right child of n
          # So we now rearrange `values` into that format...
  
          # The first step is to remove the bottom row of leaves, which might not be exactly full
          last_full_row = int(np.log2(len(values) + 1) - 1)
          len_ragged_row = len(values) - (2 ** (last_full_row + 1) - 1)
          if len_ragged_row > 0:
              bottom_row_ix = np.s_[: 2 * len_ragged_row : 2]
              tree[-len_ragged_row:] = values[bottom_row_ix]
              values = np.delete(values, bottom_row_ix)
  
          # Now `values` is length 2**n - 1, so can be packed efficiently into a tree
          # Last row of nodes is indices 0, 2, ..., 2**n - 2
          # Second-last row is indices 1, 5, ..., 2**n - 3
          # nth-last row is indices (2**n - 1)::(2**(n+1))
          values_start = 0
          values_space = 2
          values_len = 2 ** last_full_row
          while values_start < len(values):
              tree[values_len - 1 : 2 * values_len - 1] = values[values_start::values_space]
              values_start += int(values_space / 2)
              values_space *= 2
              values_len = int(values_len / 2)
          return tree
docs: Convert the np.ndarray `values` into a complete balanced tree.
  
          Assumes `values` is sorted ascending. Returns a list `t` of the same length in which t[i] >
          t[2i+1] and t[i] < t[2i+2] for all i.