code: def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
      r"""ResNeXt-50 32x4d model from
      `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      kwargs["groups"] = 32
      kwargs["width_per_group"] = 4
      return _resnet(
          "resnext50_32x4d",
          Bottleneck,
          [3, 4, 6, 3],
          pretrained,
          progress,
          **kwargs,
      )
  
imports: import torch
  import torch.nn
  from torch.hub import load_state_dict_from_url
  from ..tools.custom_modules import SequentialWithArgs, FakeReLU
label_desc: Use image node_id 9 for calling a global function with example usage: resnext50_32x4d(pretrained, progress) and returns: _resnet