code: def se_resnet50(num_classes=1000, pretrained="imagenet"):
      model = SENet(
          SEResNetBottleneck,
          [3, 4, 6, 3],
          groups=1,
          reduction=16,
          dropout_p=None,
          inplanes=64,
          input_3x3=False,
          downsample_kernel_size=1,
          downsample_padding=0,
          num_classes=num_classes,
      )
      if pretrained is not None:
          settings = pretrained_settings["se_resnet50"][pretrained]
          initialize_pretrained_model(model, num_classes, settings)
      return model
  
imports: from __future__ import print_function, division, absolute_import
  from collections import OrderedDict
  import math
  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_resnet50(num_classes, pretrained) and returns: model