class_docstr: Implementation of GAT Model
      
class_docstr_tok: ["Implementation","of","GAT","Model"]
code: def __init__(
      self,
      input_size,
      num_class,
      num_layers=1,
      feat_drop=0.6,
      attn_drop=0.6,
      num_heads=8,
      hidden_size=8,
  ):
      super(GAT, self).__init__()
      self.num_class = num_class
      self.num_layers = num_layers
      self.feat_drop = feat_drop
      self.attn_drop = attn_drop
      self.num_heads = num_heads
      self.hidden_size = hidden_size
      self.gats = nn.LayerList()
      for i in range(self.num_layers):
          if i == 0:
              self.gats.append(
                  GATConv(
                      input_size,
                      self.hidden_size,
                      self.feat_drop,
                      self.attn_drop,
                      self.num_heads,
                      activation="elu",
                  )
              )
          elif i == (self.num_layers - 1):
              self.gats.append(
                  GATConv(
                      self.num_heads * self.hidden_size,
                      self.num_class,
                      self.feat_drop,
                      self.attn_drop,
                      1,
                      concat=False,
                      activation=None,
                  )
              )
          else:
              self.gats.append(
                  GATConv(
                      self.num_heads * self.hidden_size,
                      self.hidden_size,
                      self.feat_drop,
                      self.attn_drop,
                      self.num_heads,
                      activation="elu",
                  )
              )
  
file_path: PGL/examples/gnn_depoly/models/gat.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from .base_conv import BaseConv
label_desc: Use image node_id 1 to create a new GAT object from inherited base classes: nn with example: obj = GAT(input_size, num_class, num_layers, feat_drop, attn_drop, num_heads, hidden_size)