code: def se_resnext101_32x4d(num_classes=1000):
      model = SENet(
          SEResNeXtBottleneck,
          [3, 4, 23, 3],
          groups=32,
          reduction=16,
          dropout_p=None,
          inplanes=64,
          input_3x3=False,
          downsample_kernel_size=1,
          downsample_padding=0,
          num_classes=num_classes,
      )
      settings = pretrained_settings["se_resnext101_32x4d"]["imagenet"]
      initialize_pretrained_model(model, num_classes, settings)
      return model
  
imports: from collections import OrderedDict
  import math
  from .mynn import Norm2d
  import torch.nn
  from torch.utils import model_zoo
label_desc: Use image node_id 3 for calling a global function with example usage: se_resnext101_32x4d(num_classes) and returns: model