class_docstr: Bottleneck V2 from
  `"Identity Mappings in Deep Residual Networks"
  <https://arxiv.org/abs/1603.05027>`_ paper.
  This is used for ResNet V2 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.
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code: def __init__(
      self, channels, stride, downsample=False, in_channels=0, **kwargs
  ):
      super(BottleneckV2, self).__init__(**kwargs)
      self.bn1 = nn.BatchNorm()
      self.conv1 = nn.Conv2D(
          channels // 4, kernel_size=1, strides=1, use_bias=False
      )
      self.bn2 = nn.BatchNorm()
      self.conv2 = _conv3x3(channels // 4, stride, channels // 4)
      self.bn3 = nn.BatchNorm()
      self.conv3 = nn.Conv2D(
          channels, kernel_size=1, strides=1, use_bias=False
      )
      if downsample:
          self.downsample = nn.Conv2D(
              channels,
              1,
              stride,
              use_bias=False,
              in_channels=in_channels,
          )
      else:
          self.downsample = None
  
imports: import os
  from ....device import cpu
  from ...block import HybridBlock
  from ...None import nn
  from ....None import base
  from ....util import use_np, wrap_ctx_to_device_func
  from ....None import npx
label_desc: Use image node_id 1 to create a new BottleneckV2 object from inherited base classes: HybridBlock with example: obj = BottleneckV2(channels, stride, downsample, in_channels)