Method multi_update_all

python/dgl/heterograph.py:5161–5277

r"""Send messages along all the edges, reduce them by first type-wisely then across different types, and then update the node features of all the nodes. Parameters ---------- etype_dict : dict Arguments for edge-type-wise message passing. The keys

(self, etype_dict, cross_reducer, apply_node_func=None)

    #################################################################

    def multi_update_all(self, etype_dict, cross_reducer, apply_node_func=None):
        r"""Send messages along all the edges, reduce them by first type-wisely
        then across different types, and then update the node features of all
        the nodes.

        Parameters
        ----------
        etype_dict : dict
            Arguments for edge-type-wise message passing. The keys are edge types
            while the values are message passing arguments.

            The allowed key formats are:

            * ``(str, str, str)`` for source node type, edge type and destination node type.
            * or one ``str`` edge type name if the name can uniquely identify a
              triplet format in the graph.

            The value must be a tuple ``(message_func, reduce_func, [apply_node_func])``, where

            * message_func : dgl.function.BuiltinFunction or callable
                The message function to generate messages along the edges.
                It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
            * reduce_func : dgl.function.BuiltinFunction or callable
                The reduce function to aggregate the messages.
                It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
            * apply_node_func : callable, optional
                An optional apply function to further update the node features
                after the message reduction. It must be a :ref:`apiudf`.

        cross_reducer : str or callable function
            Cross type reducer. One of ``"sum"``, ``"min"``, ``"max"``, ``"mean"``, ``"stack"``
            or a callable function. If a callable function is provided, the input argument must be
            a single list of tensors containing aggregation results from each edge type, and the
            output of function must be a single tensor.
        apply_node_func : callable, optional
            An optional apply function after the messages are reduced both
            type-wisely and across different types.
            It must be a :ref:`apiudf`.

        Notes
        -----
        DGL recommends using DGL's bulit-in function for the message_func
        and the reduce_func in the type-wise message passing arguments,
        because DGL will invoke efficient kernels that avoids copying node features to
        edge features in this case.


        Examples
        --------
        >>> import dgl
        >>> import dgl.function as fn
        >>> import torch

        Instantiate a heterograph.

        >>> g = dgl.heterograph({
        ...     ('user', 'follows', 'user'): ([0, 1], [1, 1]),
        ...     ('game', 'attracts', 'user'): ([0], [1])