code: def resnext101_32x8d(
      pretrained=False,
      filter_size=4,
      pool_only=True,
      _force_nonfinetuned=False,
      **kwargs,
  ):
      model = ResNet(
          Bottleneck,
          [3, 4, 23, 3],
          groups=32,
          width_per_group=8,
          filter_size=filter_size,
          pool_only=pool_only,
          **kwargs,
      )
      if pretrained:
          if filter_size == 4:
              model.load_state_dict(
                  model_zoo.load_url(
                      model_urls["resnext101_32x8d_lpf4_finetune"],
                      map_location="cpu",
                      check_hash=True,
                  )["state_dict"]
              )
          else:
              raise ValueError("No pretrained model available")
      return model
  
imports: import torch.nn
  import torch.utils.model_zoo
  from antialiased_cnns import *
label_desc: Use image node_id 9 for calling a global function with example usage: resnext101_32x8d(pretrained, filter_size, pool_only, _force_nonfinetuned) and returns: model