code: def __init__(self, in_channels, n_r, d_model, max_len, dropout):
      super(PRENFPN, self).__init__()
      assert len(in_channels) == 3, "in_channels' length must be 3."
      c1, c2, c3 = in_channels  # the depths are from big to small
      # build fpn
      assert d_model % 3 == 0, "{} can't be divided by 3.".format(
          d_model
      )
      self.agg_p1 = PoolAggregate(n_r, c1, d_out=d_model // 3)
      self.agg_p2 = PoolAggregate(n_r, c2, d_out=d_model // 3)
      self.agg_p3 = PoolAggregate(n_r, c3, d_out=d_model // 3)
  
      self.agg_w1 = WeightAggregate(n_r, c1, 4 * c1, d_model // 3)
      self.agg_w2 = WeightAggregate(n_r, c2, 4 * c2, d_model // 3)
      self.agg_w3 = WeightAggregate(n_r, c3, 4 * c3, d_model // 3)
  
      self.gcn_pool = GCN(d_model, n_r, d_model, max_len, dropout)
      self.gcn_weight = GCN(d_model, n_r, d_model, max_len, dropout)
  
      self.out_channels = d_model
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new PRENFPN object from inherited base classes: nn with example: obj = PRENFPN(in_channels, n_r, d_model, max_len, dropout)