code: def _make_branch(use_pool, norm_layer, norm_kwargs, *conv_settings):
      out = nn.HybridSequential(prefix="")
      if use_pool == "avg":
          out.add(nn.AvgPool3D(pool_size=3, strides=1, padding=1))
      elif use_pool == "max":
          out.add(nn.MaxPool3D(pool_size=3, strides=1, padding=1))
      setting_names = [
          "in_channels",
          "channels",
          "kernel_size",
          "strides",
          "padding",
      ]
      for setting in conv_settings:
          kwargs = {}
          for i, value in enumerate(setting):
              if value is not None:
                  if setting_names[i] == "in_channels":
                      in_channels = value
                  elif setting_names[i] == "channels":
                      channels = value
                  else:
                      kwargs[setting_names[i]] = value
          out.add(
              _make_basic_conv(
                  in_channels,
                  channels,
                  norm_layer,
                  norm_kwargs,
                  **kwargs,
              )
          )
      return out
  
imports: from mxnet import nd
  from mxnet import init
  from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.contrib.nn import HybridConcurrent
  from gluoncv.model_zoo.googlenet import googlenet
label_desc: Use image node_id 2 for calling a global function with example usage: _make_branch(use_pool, norm_layer, norm_kwargs) and returns: out