code: def forward(self, features, mask, lengths):
      _ = mask
      len_div = lengths.unsqueeze(-1).float()
      result_sum = torch.sum(features, dim=1)
      result = result_sum / len_div
      return result
  
imports: from collections import OrderedDict
  import os
  import numpy
  import torch
  from torch import nn
  from gluoncv.torch.utils.coot_utils import truncated_normal_fill
label_desc: Use image node_id 1 for calling the AvgPool obj's underlying member method code with example usage: obj.forward(features, mask, lengths) and returns: result