def METHOD_NAME(self, g, feat):
    """Forward computation
    Parameters
    ----------
    g : DGLGraph
        The graph.
    feat : tf.Tensor or pair of tf.Tensor
        :math:`(N, D)` where :math:`N` is the number of nodes and
        :math:`D` is the number of feature dimensions.
        If a pair of tensors is given, the graph must be a uni-bipartite graph
        with only one edge type, and the two tensors must have the same
        dimensionality on all except the first axis.
    Returns
    -------
    tf.Tensor or pair of tf.Tensor
        New node features.
    Raises
    ------
    DGLError
        If there are 0-in-degree nodes in the input graph, it will raise DGLError
        since no message will be passed to those nodes. This will cause invalid output.
        The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.
    """
    with g.local_scope():
        if not self._allow_zero_in_degree:
            if tf.math.count_nonzero(g.in_degrees() == 0) > 0:
                raise DGLError(
                    "There are 0-in-degree nodes in the graph, "
                    "output for those nodes will be invalid. "
                    "This is harmful for some applications, "
                    "causing silent performance regression. "
                    "Adding self-loop on the input graph by "
                    "calling `g = dgl.add_self_loop(g)` will resolve "
                    "the issue. Setting ``allow_zero_in_degree`` "
                    "to be `True` when constructing this module will "
                    "suppress the check and let the code run."
                )
        h_src, h_dst = expand_as_pair(feat, g)
        g.srcdata["x"] = h_src
        g.dstdata["x"] = h_dst
        g.apply_edges(fn.v_sub_u("x", "x", "theta"))
        g.edata["theta"] = self.theta(g.edata["theta"])
        g.dstdata["phi"] = self.phi(g.dstdata["x"])
        if not self.batch_norm:
            g.update_all(fn.e_add_v("theta", "phi", "e"), fn.max("e", "x"))
        else:
            g.apply_edges(fn.e_add_v("theta", "phi", "e"))
            # for more comments on why global batch norm instead
            # of batch norm within EdgeConv go to
            # https://github.com/dmlc/dgl/blob/master/python/dgl/nn/pytorch/conv/edgeconv.py
            g.edata["e"] = self.bn(g.edata["e"])
            g.update_all(fn.copy_e("e", "e"), fn.max("e", "x"))
        return g.dstdata["x"]