class_docstr: Bottleneck V1 from `"Deep Residual Learning for Image Recognition"
  <http://arxiv.org/abs/1512.03385>`_ paper.
  This is used for ResNet V1 for 50, 101, 152 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.
  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
  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 __init__(
      self,
      channels,
      stride,
      downsample=False,
      in_channels=0,
      last_gamma=False,
      use_se=False,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(BottleneckV1, self).__init__(**kwargs)
      self.body = nn.HybridSequential(prefix="")
      self.body.add(
          nn.Conv2D(channels // 4, kernel_size=1, strides=stride)
      )
      self.body.add(
          norm_layer(**({} if norm_kwargs is None else norm_kwargs))
      )
      self.body.add(nn.Activation("relu"))
      self.body.add(_conv3x3(channels // 4, 1, channels // 4))
      self.body.add(
          norm_layer(**({} if norm_kwargs is None else norm_kwargs))
      )
      self.body.add(nn.Activation("relu"))
      self.body.add(nn.Conv2D(channels, kernel_size=1, strides=1))
  
      if use_se:
          self.se = nn.HybridSequential(prefix="")
          self.se.add(nn.Dense(channels // 16, use_bias=False))
          self.se.add(nn.Activation("relu"))
          self.se.add(nn.Dense(channels, use_bias=False))
          self.se.add(nn.Activation("sigmoid"))
      else:
          self.se = None
  
      if not last_gamma:
          self.body.add(
              norm_layer(**({} if norm_kwargs is None else norm_kwargs))
          )
      else:
          self.body.add(
              norm_layer(
                  gamma_initializer="zeros",
                  **({} if norm_kwargs is None else norm_kwargs),
              )
          )
  
      if downsample:
          self.downsample = nn.HybridSequential(prefix="")
          self.downsample.add(
              nn.Conv2D(
                  channels,
                  kernel_size=1,
                  strides=stride,
                  use_bias=False,
                  in_channels=in_channels,
              )
          )
          self.downsample.add(
              norm_layer(**({} if norm_kwargs is None else norm_kwargs))
          )
      else:
          self.downsample = None
  
imports: from __future__ import division
  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 1 to create a new BottleneckV1 object from inherited base classes: HybridBlock with example: obj = BottleneckV1(channels, stride, downsample, in_channels, last_gamma, use_se, norm_layer, norm_kwargs)