class_docstr: Wider ResNet with pre-activation (identity mapping) blocks
  
  This variant uses down-sampling by max-pooling in the first two blocks and
   by strided convolution in the others.
  
  Parameters
  ----------
  structure : list of int
      Number of residual blocks in each of the six modules of the network.
  norm_act : callable
      Function to create normalization / activation Module.
  classes : int
      If not `0` also include global average pooling and a fully-connected layer
      with `classes` outputs at the end
      of the network.
  dilation : bool
      If `True` apply dilation to the last three modules and change the
      down-sampling factor from 32 to 8.
code: def hybrid_forward(self, F, img):
      out = self.mod1(img)
      out = self.mod2(self.pool2(out))
      out = self.mod3(self.pool3(out))
      out = self.mod4(out)
      out = self.mod5(out)
      out = self.mod6(out)
      out = self.mod7(out)
      out = self.bn_out(out)
  
      if hasattr(self, "classifier"):
          return self.classifier(out)
      return out
  
file_path: gluon-cv/gluoncv/model_zoo/wideresnet.py
imports: import sys
  from collections import OrderedDict
  from functools import partial
  from mxnet.gluon import nn
  from mxnet.gluon.nn import HybridBlock
label_desc: Use image node_id 2 for calling the WiderResNetA2 obj's underlying member method code with example usage: obj.hybrid_forward(F, img) and returns: out, self