code: def shufflenet_v2_x0_5(
      *,
      weights: Optional[ShuffleNet_V2_X0_5_Weights] = None,
      progress: bool = True,
      **kwargs: Any,
  ) -> ShuffleNetV2:
      """
      Constructs a ShuffleNetV2 architecture with 0.5x output channels, as described in
      `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
      <https://arxiv.org/abs/1807.11164>`__.
  
      Args:
          weights (:class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): The
              pretrained weights to use. See
              :class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights` below for
              more details, and possible values. By default, no pre-trained
              weights are used.
          progress (bool, optional): If True, displays a progress bar of the
              download to stderr. Default is True.
          **kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``
              base class. Please refer to the `source code
              <https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py>`_
              for more details about this class.
  
      .. autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights
          :members:
      """
      weights = ShuffleNet_V2_X0_5_Weights.verify(weights)
  
      return _shufflenetv2(
          weights,
          progress,
          [4, 8, 4],
          [24, 48, 96, 192, 1024],
          **kwargs,
      )
  
imports: from functools import partial
  from typing import Any, Callable, List, Optional
  import torch
  import torch.nn
  from torch import Tensor
  from ..transforms._presets import ImageClassification
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 3 for calling a global function with example usage: shufflenet_v2_x0_5() and returns: _shufflenetv2