code: def gini(w: torch.Tensor) -> torch.Tensor:
      r"""The Gini coefficient from the `"Improving Molecular Graph Neural
      Network Explainability with Orthonormalization and Induced Sparsity"
      <https://arxiv.org/abs/2105.04854>`_ paper.
  
      Computes a regularization penalty :math:`\in [0, 1]` for each row of a
      matrix according to
  
      .. math::
          \mathcal{L}_\textrm{Gini}^i = \sum_j^n \sum_{j'}^n \frac{|w_{ij}
           - w_{ij'}|}{2 (n^2 - n)\bar{w_i}}
  
      and returns an average over all rows.
  
      Args:
          w (torch.Tensor): A two-dimensional tensor.
      """
      s = 0
      for row in w:
          t = row.repeat(row.size(0), 1)
          u = (t - t.T).abs().sum() / (
              2 * (row.size(-1) ** 2 - row.size(-1)) * row.abs().mean()
              + torch.finfo().eps
          )
          s += u
      s /= w.shape[0]
      return s
  
label_desc: Use image node_id 1 for calling a global function with example usage: gini(w) and returns: s