code: def shufflenet_v2_x0_5(num_classes=1000):
      """
      Constructs a ShuffleNetV2 with 0.5x output channels, as described in
      `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
      <https://arxiv.org/abs/1807.11164>`.
      weight: https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth
  
      :param num_classes:
      :return:
      """
      model = ShuffleNetV2(
          stages_repeats=[4, 8, 4],
          stages_out_channels=[24, 48, 96, 192, 1024],
          num_classes=num_classes,
      )
  
      return model
  
file_path: deep-learning-for-image-processing/pytorch_classification/mini_imagenet/model.py
imports: from typing import List, Callable
  import torch
  from torch import Tensor
  import torch.nn
label_desc: Use image node_id 3 for calling a global function with example usage: shufflenet_v2_x0_5(num_classes) and returns: model