code: def group_bottleneck_block(
      self, blob_in, dim_in, dim_out, stride, prefix, dim_inner, group
  ):
      blob_out = self.conv_bn_relu(
          blob_in,
          dim_in,
          dim_inner,
          1,
          1,
          prefix + "_branch2a",
          pad=0,
      )
      blob_out = self.conv_bn_relu(
          blob_out,
          dim_inner,
          dim_inner,
          3,
          stride,
          prefix + "_branch2b",
          group=group,
      )
      bn_blob = self.conv_bn(
          blob_out,
          dim_inner,
          dim_out,
          1,
          1,
          prefix + "_branch2c",
          pad=0,
      )
      if self.opts["model_param"]["custom_bn_init"]:
          self.model.param_init_net.ConstantFill(
              [bn_blob + "_s"],
              bn_blob + "_s",
              value=self.opts["model_param"]["bn_init_gamma"],
          )
  
      sc_blob = self.add_shortcut(
          blob_in, dim_in, dim_out, stride, prefix=prefix + "_branch1"
      )
      sum_blob = self.model.net.Sum([bn_blob, sc_blob], prefix + "_sum")
      return self.model.Relu(sum_blob, sum_blob)
  
file_path: pytorch/caffe2/contrib/playground/resnetdemo/explicit_resnet_forward.py
imports: import logging
label_desc: Use image node_id 6 for calling the ResNetModelHelper obj's underlying member method code with example usage: obj.group_bottleneck_block(blob_in, dim_in, dim_out, stride, prefix, dim_inner, group) and returns: self