parameters: (self, x, neighbor, expand)
return_statement: return tf.reduce_mean(rets, axis=0)
docstring: Compute node embeddings based on GAT.
      Args:
        x: A float tensor with shape = [batch_size, in_dim].
        neighbor: A float tensor with shape = [batch_size * expand, in_dim].
        expand: An integer, the neighbor count.
  
      Return:
        A float tensor with shape=[batch_size, out_dim].
docstring_summary: Compute node embeddings based on GAT.
      Args:
        x: A float tensor with shape = [batch_size, in_dim].
        neighbor: A float tensor with shape = [batch_size * expand, in_dim].
        expand: An integer, the neighbor count.
docstring_tokens: Compute node embeddings based on GAT . Args : x : A float tensor with shape = [ batch_size in_dim ] . neighbor : A float tensor with shape = [ batch_size * expand in_dim ] . expand : An integer the neighbor count .
function: def forward(self, x, neighbor, expand):
      """ Compute node embeddings based on GAT.
      Args:
        x: A float tensor with shape = [batch_size, in_dim].
        neighbor: A float tensor with shape = [batch_size * expand, in_dim].
        expand: An integer, the neighbor count.
  
      Return:
        A float tensor with shape=[batch_size, out_dim].
      """
      rets = [self.singel_head_conv(x, neighbor, expand, i) for i in range(self._num_head)]
      return tf.reduce_mean(rets, axis=0)
function_tokens: def forward ( self , x , neighbor , expand ) : rets = [ self . singel_head_conv ( x , neighbor , expand , i ) for i in range ( self . _num_head ) ] return tf . reduce_mean ( rets , axis = 0 )