class_docstr: BasicBlock V2 from
  `"Identity Mappings in Deep Residual Networks"
  <https://arxiv.org/abs/1603.05027>`_ paper.
  This is used for ResNet V2 for 18, 34 layers.
  
  Parameters
  ----------
  channels : int
      Number of output channels.
  stride : int
      Stride size.
  downsample : bool, default False
      Whether to downsample the input.
  in_channels : int, default 0
      Number of input channels. Default is 0, to infer from the graph.
  norm_layer : object
      Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
      Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  norm_kwargs : dict
      Additional `norm_layer` arguments, for example `num_devices=4`
      for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
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docstr: Hybrid forward
docstr_tok: ["Hybrid","forward"]
code: def hybrid_forward(self, F, x):
      """Hybrid forward"""
      residual = x
  
      x = self.bn1(x)
      x = F.Activation(x, act_type="relu")
      x = self.conv1(x)
  
      x = self.bn2(x)
      x = F.Activation(x, act_type="relu")
      x = self.conv2(x)
  
      if self.downsample:
          residual = self.downsample(residual)
      return x + residual
  
imports: from __future__ import division
  import os
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet import cpu
label_desc: Use image node_id 2 for calling the CIFARBasicBlockV2 obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: unknown