class_docstr: Bottleneck Block from `"Aggregated Residual Transformations for Deep Neural Network"
  <http://arxiv.org/abs/1611.05431>`_ paper.
  
  Parameters
  ----------
  cardinality: int
      Number of groups
  bottleneck_width: int
      Width of bottleneck block
  stride : int
      Stride size.
  downsample : bool, default False
      Whether to downsample the input.
  last_gamma : bool, default False
      Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
  use_se : bool, default False
      Whether to use Squeeze-and-Excitation module
  avg_down : bool, default False
      Whether to use average pooling for projection skip connection between stages/downsample.
  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`.
code: def hybrid_forward(self, F, x):
      residual = x
  
      x = self.body(x)
  
      if self.se:
          w = F.contrib.AdaptiveAvgPooling2D(x, output_size=1)
          w = self.se(w)
          x = F.broadcast_mul(x, w)
  
      if self.downsample:
          residual = self.downsample(residual)
  
      x = F.Activation(x + residual, act_type="relu")
      return x
  
imports: from __future__ import division
  import os
  import math
  from mxnet import cpu
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
label_desc: Use image node_id 2 for calling the Block obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x