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.
class_docstr_tok: ["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","."]
code: def hybrid_forward(self, F, x):
      residual = x
      x = self.body(x)
      if self.downsample:
          residual = self.downsample(residual)
      x = F.Activation(x + residual, act_type="relu")
      return x
  
imports: from __future__ import absolute_import
  import os
  import mxnet
  from mxnet import autograd
  from mxnet.gluon import nn
  from mxnet.gluon.contrib.nn import SyncBatchNorm
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
  from .rcnn_target import RCNNTargetSampler, RCNNTargetGenerator
  from ..rcnn import custom_rcnn_fpn
  from ....model_zoo.rcnn import RCNN
  from ....model_zoo.rcnn.rpn import RPN
label_desc: Use image node_id 2 for calling the BottleneckV1 obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x