class_docstr: Pre-activation residual block
      Identity Mapping in Deep Residual Networks
      ref https://arxiv.org/abs/1603.05027
  
  Args:
     inplanes(int): Number of input channels.
     planes(int): Number of output channels.
     stride(int): Number of stride.
     downsample(int): Scale factor for downsample layer, default is None.
     norm_layer(nn.Layer): Batch norm layer, default is nn.BatchNorm2D.
  
  Return:
      img(paddle.Tensor): Bottleneck output.
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code: def __init__(
      self,
      inplanes: int,
      planes: int,
      stride: int = 1,
      downsample: int = None,
      norm_layer: nn.Layer = nn.BatchNorm2D,
  ):
      super(Bottleneck, self).__init__()
      self.expansion = 4
      self.downsample = downsample
      if self.downsample is not None:
          self.residual_layer = nn.Conv2D(
              inplanes,
              planes * self.expansion,
              kernel_size=1,
              stride=stride,
          )
      conv_block = (
          norm_layer(inplanes),
          nn.ReLU(),
          nn.Conv2D(inplanes, planes, kernel_size=1, stride=1),
          norm_layer(planes),
          nn.ReLU(),
          ConvLayer(planes, planes, kernel_size=3, stride=stride),
          norm_layer(planes),
          nn.ReLU(),
          nn.Conv2D(
              planes, planes * self.expansion, kernel_size=1, stride=1
          ),
      )
      self.conv_block = nn.Sequential(*conv_block)
  
imports: import os
  import argparse
  import paddle
  import paddle.nn
  import numpy
  import paddle.nn.functional
  from paddlehub.env import MODULE_HOME
  from paddlehub.module.module import moduleinfo
  from paddlehub.vision.transforms import Compose, Resize, CenterCrop
  from paddlehub.module.cv_module import StyleTransferModule
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(inplanes, planes, stride, downsample, norm_layer)