code: def forward(self, x):
      b = x.shape[0]
      outs = []
      for agg in self.aggs:
          y = agg(x)
          p = F.adaptive_avg_pool2d(y, 1)
          outs.append(p.reshape((b, 1, self.d_out)))
      out = paddle.concat(outs, 1)
      return out
  
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 3 for calling the PoolAggregate obj's underlying member method code with example usage: obj.forward(x) and returns: out