class_docstr: ResNet Bottleneck Block for R2Plus1D
  
  Parameters
  ----------
  inplanes : int.
      Input channels of each block.
  planes : int.
      Output channels of each block.
  stride : int, default is 1.
      Stride in each dimension of 3D convolutional layers in a block.
  downsample : bool.
      Whether to contain a downsampling layer in the block.
  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`.
  layer_name : str, default is ''.
      Give a name to current block.
class_docstr_tok: ["ResNet","Bottleneck","Block","for","R2Plus1D","Parameters","--","--","--","--","--","inplanes",":","int",".","Input","channels","of","each","block",".","planes",":","int",".","Output","channels","of","each","block",".","stride",":","int",",","default","is","1",".","Stride","in","each","dimension","of","3D","convolutional","layers","in","a","block",".","downsample",":","bool",".","Whether","to","contain","a","downsampling","layer","in","the","block",".","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","`",".","layer_name",":","str",",","default","is","``",".","Give","a","name","to","current","block","."]
docstr: Hybrid forward of a ResBlock in R2+1D.
docstr_tok: ["Hybrid","forward","of","a","ResBlock","in","R2+1D","."]
code: def hybrid_forward(self, F, x):
      """Hybrid forward of a ResBlock in R2+1D."""
      identity = x
      out = self.conv1(x)
      out = self.bn1(out)
      out = self.relu(out)
  
      out = self.conv2(out)
      out = self.bn2(out)
      out = self.relu(out)
  
      out = self.conv3(out)
      out = self.bn3(out)
  
      if self.downsample is not None:
          identity = self.downsample(x)
  
      out = F.Activation(out + identity, act_type="relu")
      return out
  
imports: from mxnet import init
  from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
label_desc: Use image node_id 2 for calling the Bottleneck obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: out