class_docstr: ResNeSt Bottleneck
      
class_docstr_tok: ["ResNeSt","Bottleneck"]
code: def hybrid_forward(self, F, x):
      residual = x
  
      out = self.conv1(x)
      out = self.bn1(out)
      if self.dropblock_prob > 0:
          out = self.dropblock1(out)
      out = self.relu1(out)
  
      if self.avd and self.avd_first:
          out = self.avd_layer(out)
  
      if self.use_splat:
          out = self.conv2(out)
          if self.dropblock_prob > 0:
              out = self.dropblock2(out)
      else:
          out = self.conv2(out)
          out = self.bn2(out)
          if self.dropblock_prob > 0:
              out = self.dropblock2(out)
          out = self.relu2(out)
  
      if self.avd and not self.avd_first:
          out = self.avd_layer(out)
  
      out = self.conv3(out)
      out = self.bn3(out)
  
      if self.downsample is not None:
          residual = self.downsample(x)
  
      if self.dropblock_prob > 0:
          out = self.dropblock3(out)
  
      out = out + residual
      out = self.relu3(out)
  
      return out
  
imports: from __future__ import division
  import math
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon.nn import BatchNorm
  from ..nn.dropblock import DropBlock
  from ..nn.splat import SplitAttentionConv
label_desc: Use image node_id 2 for calling the Bottleneck obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: out