[Feature] Support multidimensional features for GAT

[BarclayII]

This PR enables GAT for inputs with axes greater than 2. The computation will parallelize on the second to second-to-last axes.

Useful for batch computation on graphs with the same structure. From now on, people can just do:

x = torch.randn(num_nodes, batch_size, feat_dim)
m = dglnn.GATConv(feat_dim, out_dim, num_heads)
y = m(g, x)   # gives a (num_nodes, batch_size, num_heads, out_dim) tensor

Note that modules like GraphConv and SAGEConv already supports such semantics.

[dgl-bot]

To trigger regression tests:

• @dgl-bot run [instance-type] [which tests] [compare-with-branch];
  For example: @dgl-bot run g4dn.4xlarge all dmlc/master or @dgl-bot run c5.9xlarge kernel,api dmlc/master