code: def resnext101_64x4d(
      *,
      weights: Optional[
          Union[
              ResNeXt101_64X4D_QuantizedWeights,
              ResNeXt101_64X4D_Weights,
          ]
      ] = None,
      progress: bool = True,
      quantize: bool = False,
      **kwargs: Any,
  ) -> QuantizableResNet:
      """ResNeXt-101 64x4d model from
      `Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_
  
      .. note::
          Note that ``quantize = True`` returns a quantized model with 8 bit
          weights. Quantized models only support inference and run on CPUs.
          GPU inference is not yet supported.
  
      Args:
          weights (:class:`~torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The
              pretrained weights for the model. See
              :class:`~torchvision.models.quantization.ResNet101_64X4D_QuantizedWeights` 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.
          quantize (bool, optional): If True, return a quantized version of the model. Default is False.
          **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
              base class. Please refer to the `source code
              <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
              for more details about this class.
  
      .. autoclass:: torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights
          :members:
  
      .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights
          :members:
          :noindex:
      """
      weights = (
          ResNeXt101_64X4D_QuantizedWeights
          if quantize
          else ResNeXt101_64X4D_Weights
      ).verify(weights)
  
      _ovewrite_named_param(kwargs, "groups", 64)
      _ovewrite_named_param(kwargs, "width_per_group", 4)
      return _resnet(
          QuantizableBottleneck,
          [3, 4, 23, 3],
          weights,
          progress,
          quantize,
          **kwargs,
      )
  
imports: from functools import partial
  from typing import Any, List, Optional, Type, Union
  import torch
  import torch.nn
  from torch import Tensor
  from torchvision.models.resnet import BasicBlock, Bottleneck, ResNet, ResNet18_Weights, ResNet50_Weights, ResNeXt101_32X8D_Weights, ResNeXt101_64X4D_Weights
  from ...transforms._presets import ImageClassification
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _IMAGENET_CATEGORIES
  from .._utils import _ovewrite_named_param, handle_legacy_interface
  from .utils import _fuse_modules, _replace_relu, quantize_model
label_desc: Use image node_id 5 for calling a global function with example usage: resnext101_64x4d() and returns: _resnet