code: def __call__(self, x):
      x = self.conv(x)
      fpn_list = []
      F = []
      for i in range(len(self.depth)):
          fpn_list.append(np.sum(self.depth[: i + 1]))
  
      for i, block in enumerate(self.block_list):
          x = block(x)
          for number in fpn_list:
              if i + 1 == number:
                  F.append(x)
      base = F[-1]
  
      j = 0
      for i, block in enumerate(self.base_block):
          if i % 3 == 0 and i < 6:
              j = j + 1
              b, c, w, h = F[-j - 1].shape
              if [w, h] == list(base.shape[2:]):
                  base = base
              else:
                  base = self.conv_trans[j - 1](base)
                  base = self.bn_block[j - 1](base)
              base = paddle.concat([base, F[-j - 1]], axis=1)
          base = block(base)
      return base
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from paddle import nn, ParamAttr
  from paddle.nn import functional
  import paddle
  import numpy
label_desc: Use image node_id 2 for calling the ResNetFPN obj's underlying member method code with example usage: obj.__call__(x) and returns: base