code: def forward(self, x):
      x = self.features(x)
      if not self.training and self.test_time_pool:
          x = F.avg_pool2d(x, kernel_size=7, stride=1)
          out = self.last_linear(x)
          # The extra test time pool should be pooling an img_size//32 - 6 size patch
          out = adaptive_avgmax_pool2d(out, pool_type="avgmax")
      else:
          x = adaptive_avgmax_pool2d(x, pool_type="avg")
          out = self.last_linear(x)
      return out.view(out.size(0), -1)
  
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
  from collections import OrderedDict
  from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
  from .convert_from_mxnet import convert_from_mxnet, has_mxnet
label_desc: Use image node_id 2 for calling the DPN obj's underlying member method code with example usage: obj.forward(x) and returns: out