code: def edge_softmax(graph, logits, eids=ALL, norm_by="dst"):
      r"""Compute softmax over weights of incoming edges for every node.
  
      For a node :math:`i`, edge softmax is an operation that computes
  
      .. math::
        a_{ij} = \frac{\exp(z_{ij})}{\sum_{j\in\mathcal{N}(i)}\exp(z_{ij})}
  
      where :math:`z_{ij}` is a signal of edge :math:`j\rightarrow i`, also
      called logits in the context of softmax. :math:`\mathcal{N}(i)` is
      the set of nodes that have an edge to :math:`i`.
  
      By default edge softmax is normalized by destination nodes(i.e. :math:`ij`
      are incoming edges of `i` in the formula above). We also support edge
      softmax normalized by source nodes(i.e. :math:`ij` are outgoing edges of
      `i` in the formula). The former case corresponds to softmax in GAT and
      Transformer, and the latter case corresponds to softmax in Capsule network.
      An example of using edge softmax is in
      `Graph Attention Network <https://arxiv.org/pdf/1710.10903.pdf>`__ where
      the attention weights are computed with this operation.
      Other non-GNN examples using this are
      `Transformer <https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf>`__,
      `Capsule <https://arxiv.org/pdf/1710.09829.pdf>`__, etc.
  
      Parameters
      ----------
      graph : DGLGraph
          The graph over which edge softmax will be performed.
      logits : torch.Tensor or dict of torch.Tensor
          The input edge feature. Heterogeneous graphs can have dict of tensors where
          each tensor stores the edge features of the corresponding relation type.
      eids : torch.Tensor or ALL, optional
          The IDs of the edges to apply edge softmax. If ALL, it will apply edge
          softmax to all edges in the graph. Default: ALL.
      norm_by : str, could be `src` or `dst`
          Normalized by source nodes or destination nodes. Default: `dst`.
  
      Returns
      -------
      Tensor or tuple of tensors
          Softmax value.
  
      Notes
      -----
          * Input shape: :math:`(E, *, 1)` where * means any number of
            additional dimensions, :math:`E` equals the length of eids.
            If the `eids` is ALL, :math:`E` equals the number of edges in
            the graph.
          * Return shape: :math:`(E, *, 1)`
  
      Examples on a homogeneous graph
      -------------------------------
      The following example uses PyTorch backend.
  
      >>> from dgl.nn.functional import edge_softmax
      >>> import dgl
      >>> import torch as th
  
      Create a :code:`DGLGraph` object and initialize its edge features.
  
      >>> g = dgl.graph((th.tensor([0, 0, 0, 1, 1, 2]), th.tensor([0, 1, 2, 1, 2, 2])))
      >>> edata = th.ones(6, 1).float()
      >>> edata
          tensor([[1.],
                  [1.],
                  [1.],
                  [1.],
                  [1.],
                  [1.]])
  
      Apply edge softmax over g:
  
      >>> edge_softmax(g, edata)
          tensor([[1.0000],
                  [0.5000],
                  [0.3333],
                  [0.5000],
                  [0.3333],
                  [0.3333]])
  
      Apply edge softmax over g normalized by source nodes:
  
      >>> edge_softmax(g, edata, norm_by='src')
          tensor([[0.3333],
                  [0.3333],
                  [0.3333],
                  [0.5000],
                  [0.5000],
                  [1.0000]])
  
      Apply edge softmax to first 4 edges of g:
  
      >>> edge_softmax(g, edata[:4], th.Tensor([0,1,2,3]))
          tensor([[1.0000],
                  [0.5000],
                  [1.0000],
                  [0.5000]])
  
  
      Examples on a heterogeneous graph
      ---------------------------------
  
      Create a heterogeneous graph and initialize its edge features.
  
      >>> hg = dgl.heterograph({
      ...     ('user', 'follows', 'user'): ([0, 0, 1], [0, 1, 2]),
      ...     ('developer', 'develops', 'game'): ([0, 1], [0, 1])
      ...     })
      >>> edata_follows = th.ones(3, 1).float()
      >>> edata_develops = th.ones(2, 1).float()
      >>> edata_dict = {('user', 'follows', 'user'): edata_follows,
      ... ('developer','develops', 'game'): edata_develops}
  
      Apply edge softmax over hg normalized by source nodes:
  
      >>> edge_softmax(hg, edata_dict, norm_by='src')
          {('developer', 'develops', 'game'): tensor([[1.],
          [1.]]), ('user', 'follows', 'user'): tensor([[0.5000],
          [0.5000],
          [1.0000]])}
      """
      if not is_all(eids):
          eids = astype(eids, graph.idtype)
      if graph._graph.number_of_etypes() == 1:
          return edge_softmax_internal(
              graph._graph, logits, eids=eids, norm_by=norm_by
          )
      else:
          logits_list = [None] * graph._graph.number_of_etypes()
          logits = {
              graph.to_canonical_etype(k): v for k, v in logits.items()
          }
          for rel in graph.canonical_etypes:
              etid = graph.get_etype_id(rel)
              logits_list[etid] = logits[rel]
          logits_tuple = tuple(logits_list)
          score_tuple = edge_softmax_hetero_internal(
              graph._graph, eids, norm_by, *logits_tuple
          )
          score = {}
          for rel in graph.canonical_etypes:
              etid = graph.get_etype_id(rel)
              score[rel] = score_tuple[etid]
          return score
  
label_desc: Use image node_id 1 for calling a global function with example usage: edge_softmax(graph, logits, eids, norm_by) and returns: edge_softmax_internal, score