code: def forward(self, x):
      out_list = []
      x = self.conv1(x)
  
      x = self.blocks2(x)
      x = self.blocks3(x)
      out_list.append(x)
      x = self.blocks4(x)
      out_list.append(x)
      x = self.blocks5(x)
      out_list.append(x)
      x = self.blocks6(x)
      out_list.append(x)
  
      if self.det:
          out_list[0] = self.layer_list[0](out_list[0])
          out_list[1] = self.layer_list[1](out_list[1])
          out_list[2] = self.layer_list[2](out_list[2])
          out_list[3] = self.layer_list[3](out_list[3])
          return out_list
  
      if self.training:
          x = F.adaptive_avg_pool2d(x, [1, 40])
      else:
          x = F.avg_pool2d(x, [3, 2])
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import Constant, KaimingNormal
  from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Hardsigmoid, Hardswish, Identity, Linear, ReLU
  from paddle.regularizer import L2Decay
label_desc: Use image node_id 2 for calling the PPLCNetV3 obj's underlying member method code with example usage: obj.forward(x) and returns: x, out_list