code: def normalized_cut(
      edge_index: Tensor,
      edge_attr: Tensor,
      num_nodes: Optional[int] = None,
  ) -> Tensor:
      r"""Computes the normalized cut :math:`\mathbf{e}_{i,j} \cdot
      \left( \frac{1}{\deg(i)} + \frac{1}{\deg(j)} \right)` of a weighted graph
      given by edge indices and edge attributes.
  
      Args:
          edge_index (LongTensor): The edge indices.
          edge_attr (Tensor): Edge weights or multi-dimensional edge features.
          num_nodes (int, optional): The number of nodes, *i.e.*
              :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
  
      :rtype: :class:`Tensor`
  
      Example:
          >>> edge_index = torch.tensor([[1, 1, 2, 3],
          ...                            [3, 3, 1, 2]])
          >>> edge_attr = torch.tensor([1., 1., 1., 1.])
          >>> normalized_cut(edge_index, edge_attr)
          tensor([1.5000, 1.5000, 2.0000, 1.5000])
      """
      row, col = edge_index[0], edge_index[1]
      deg = 1.0 / degree(col, num_nodes, edge_attr.dtype)
      deg = deg[row] + deg[col]
      cut = edge_attr * deg
      return cut
  
label_desc: Use image node_id 1 for calling a global function with example usage: normalized_cut(edge_index, edge_attr, num_nodes) and returns: cut