class_docstr: Mobilenet with DUC upsampling block.
  
  Parameters
  ----------
  base_network : str
      Name of the base feature extraction network.
  up_filters : list of int
      Number of filters for DUC layers.
  up_scales : list of int
      Upsampling scales for DUC layers.
  pretrained_base : bool
      Whether load pretrained base network.
  norm_layer : mxnet.gluon.nn.HybridBlock
      Type of Norm layers, can be BatchNorm, SyncBatchNorm, GroupNorm, etc.
  norm_kwargs : dict
      Additional kwargs for `norm_layer`.
class_docstr_tok: ["Mobilenet","with","DUC","upsampling","block",".","Parameters","--","--","--","--","--","base_network",":","str","Name","of","the","base","feature","extraction","network",".","up_filters",":","list","of","int","Number","of","filters","for","DUC","layers",".","up_scales",":","list","of","int","Upsampling","scales","for","DUC","layers",".","pretrained_base",":","bool","Whether","load","pretrained","base","network",".","norm_layer",":","mxnet.gluon.nn.HybridBlock","Type","of","Norm","layers",",","can","be","BatchNorm",",","SyncBatchNorm",",","GroupNorm",",","etc",".","norm_kwargs",":","dict","Additional","kwargs","for","`","norm_layer","`","."]
docstr: HybridForward
docstr_tok: ["HybridForward"]
code: def hybrid_forward(self, F, x):
      """HybridForward"""
      y = self.feature(x)
      out = self.upsampling(y)
      return out
  
file_path: gluon-cv/gluoncv/model_zoo/center_net/duc_mobilenet.py
imports: from __future__ import absolute_import
  import warnings
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from ..model_zoo import get_model
  from ...nn.block import DUC
label_desc: Use image node_id 2 for calling the DUCMobilenet obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: out