code: def call(self, inputs):
      h = inputs
      for l in range(self.num_layers):
          h = self.gat_layers[l](self.g, h)
          h = tf.reshape(h, (h.shape[0], -1))
      # output projection
      logits = tf.reduce_mean(self.gat_layers[-1](self.g, h), axis=1)
      return logits
  
file_path: dgl/examples/tensorflow/gat/gat.py
imports: import dgl.function
  import tensorflow
  from dgl.nn import GATConv
  from tensorflow.keras import layers
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.call(inputs) and returns: logits