text: """
  Graph Attention Networks in DGL using SPMV optimization.
  References
  ----------
  Paper: https://arxiv.org/abs/1710.10903
  Author's code: https://github.com/PetarV-/GAT
  Pytorch implementation: https://github.com/Diego999/pyGAT
  """
  
  import torch
  import torch.nn as nn
  import dgl.function as fn
  from dgl.nn import GATConv
  
  
  class GAT(nn.Module):
      def __init__(self,
                   g,
                   num_layers,
                   in_dim,
                   num_hidden,
                   num_classes,
                   heads,
                   activation,
                   feat_drop,
                   attn_drop,
                   negative_slope,
                   residual):
          super(GAT, self).__init__()
          self.g = g
          self.num_layers = num_layers
          self.gat_layers = nn.ModuleList()
          self.activation = activation
          if num_layers > 1:
          # input projection (no residual)
              self.gat_layers.append(GATConv(
                  in_dim, num_hidden, heads[0],
                  feat_drop, attn_drop, negative_slope, False, self.activation))
              # hidden layers
              for l in range(1, num_layers-1):
                  # due to multi-head, the in_dim = num_hidden * num_heads
                  self.gat_layers.append(GATConv(
                      num_hidden * heads[l-1], num_hidden, heads[l],
                      feat_drop, attn_drop, negative_slope, residual, self.activation))
              # output projection
              self.gat_layers.append(GATConv(
                  num_hidden * heads[-2], num_classes, heads[-1],
                  feat_drop, attn_drop, negative_slope, residual, None))
          else:
              self.gat_layers.append(GATConv(
                  in_dim, num_classes, heads[0],
                  feat_drop, attn_drop, negative_slope, residual, None))
  
      def forward(self, inputs):
          h = inputs
          for l in range(self.num_layers):
              h = self.gat_layers[l](self.g, h).flatten(1)
          # output projection
          logits = self.gat_layers[-1](self.g, h).mean(1)
          return logits
  