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[Example] Add DimeNet(++) for Molecular Graph Property Prediction (#2706)  
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import torch
import torch.nn as nn
import dgl.function as fn

from modules.residual_layer import ResidualLayer
from modules.initializers import GlorotOrthogonal

class InteractionBlock(nn.Module):
    def __init__(self,
                 emb_size,
                 num_radial,
                 num_spherical,
                 num_bilinear,
                 num_before_skip,
                 num_after_skip,
                 activation=None):
        super(InteractionBlock, self).__init__()

        self.activation = activation
        # Transformations of Bessel and spherical basis representations
        self.dense_rbf = nn.Linear(num_radial, emb_size, bias=False)
        self.dense_sbf = nn.Linear(num_radial * num_spherical, num_bilinear, bias=False)
        # Dense transformations of input messages
        self.dense_ji = nn.Linear(emb_size, emb_size)
        self.dense_kj = nn.Linear(emb_size, emb_size)
        # Bilinear layer
        bilin_initializer = torch.empty((emb_size, num_bilinear, emb_size)).normal_(mean=0, std=2 / emb_size)
        self.W_bilin = nn.Parameter(bilin_initializer)
        # Residual layers before skip connection
        self.layers_before_skip = nn.ModuleList([
            ResidualLayer(emb_size, activation=activation) for _ in range(num_before_skip)
        ])
        self.final_before_skip = nn.Linear(emb_size, emb_size)
        # Residual layers after skip connection
        self.layers_after_skip = nn.ModuleList([
            ResidualLayer(emb_size, activation=activation) for _ in range(num_after_skip)
        ])

        self.reset_params()
    
    def reset_params(self):
        GlorotOrthogonal(self.dense_rbf.weight)
        GlorotOrthogonal(self.dense_sbf.weight)
        GlorotOrthogonal(self.dense_ji.weight)
        GlorotOrthogonal(self.dense_kj.weight)
        GlorotOrthogonal(self.final_before_skip.weight)

    def edge_transfer(self, edges):
        # Transform from Bessel basis to dence vector
        rbf = self.dense_rbf(edges.data['rbf'])
        # Initial transformation
        x_ji = self.dense_ji(edges.data['m'])
        x_kj = self.dense_kj(edges.data['m'])
        if self.activation is not None:
            x_ji = self.activation(x_ji)
            x_kj = self.activation(x_kj)

        # w: W * e_RBF \bigodot \sigma(W * m + b)
        return {'x_kj': x_kj * rbf, 'x_ji': x_ji}

    def msg_func(self, edges):
        sbf = self.dense_sbf(edges.data['sbf'])
        # Apply bilinear layer to interactions and basis function activation
        # [None, 8] * [128, 8, 128] * [None, 128] -> [None, 128]
        x_kj = torch.einsum("wj,wl,ijl->wi", sbf, edges.src['x_kj'], self.W_bilin)
        return {'x_kj': x_kj}

    def forward(self, g, l_g):
        g.apply_edges(self.edge_transfer)
        
        # nodes correspond to edges and edges correspond to nodes in the original graphs
        # node: d, rbf, o, rbf_env, x_kj, x_ji
        for k, v in g.edata.items():
            l_g.ndata[k] = v

        l_g.update_all(self.msg_func, fn.sum('x_kj', 'm_update'))

        for k, v in l_g.ndata.items():
            g.edata[k] = v

        # Transformations before skip connection
        g.edata['m_update'] = g.edata['m_update'] + g.edata['x_ji']
        for layer in self.layers_before_skip:
            g.edata['m_update'] = layer(g.edata['m_update'])
        g.edata['m_update'] = self.final_before_skip(g.edata['m_update'])
        if self.activation is not None:
            g.edata['m_update'] = self.activation(g.edata['m_update'])

        # Skip connection
        g.edata['m'] = g.edata['m'] + g.edata['m_update']

        # Transformations after skip connection
        for layer in self.layers_after_skip:
            g.edata['m'] = layer(g.edata['m'])

        return g