code: def mobilenet_v3_large(
      *,
      weights: Optional[
          Union[
              MobileNet_V3_Large_QuantizedWeights,
              MobileNet_V3_Large_Weights,
          ]
      ] = None,
      progress: bool = True,
      quantize: bool = False,
      **kwargs: Any,
  ) -> QuantizableMobileNetV3:
      """
      MobileNetV3 (Large) model from
      `Searching for MobileNetV3 <https://arxiv.org/abs/1905.02244>`_.
  
      .. 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.MobileNet_V3_Large_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The
              pretrained weights for the model. See
              :class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` below for
              more details, and possible values. By default, no pre-trained
              weights are used.
          progress (bool): If True, displays a progress bar of the
              download to stderr. Default is True.
          quantize (bool): If True, return a quantized version of the model. Default is False.
          **kwargs: parameters passed to the ``torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights``
              base class. Please refer to the `source code
              <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/mobilenetv3.py>`_
              for more details about this class.
  
      .. autoclass:: torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights
          :members:
      .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights
          :members:
          :noindex:
      """
      weights = (
          MobileNet_V3_Large_QuantizedWeights
          if quantize
          else MobileNet_V3_Large_Weights
      ).verify(weights)
  
      inverted_residual_setting, last_channel = _mobilenet_v3_conf(
          "mobilenet_v3_large", **kwargs
      )
      return _mobilenet_v3_model(
          inverted_residual_setting,
          last_channel,
          weights,
          progress,
          quantize,
          **kwargs,
      )
  
imports: from functools import partial
  from typing import Any, List, Optional, Union
  import torch
  from torch import nn, Tensor
  from torch.ao.quantization import DeQuantStub, QuantStub
  from ...ops.misc import Conv2dNormActivation, SqueezeExcitation
  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 ..mobilenetv3 import _mobilenet_v3_conf, InvertedResidual, InvertedResidualConfig, MobileNet_V3_Large_Weights, MobileNetV3
  from .utils import _fuse_modules, _replace_relu
label_desc: Use image node_id 2 for calling a global function with example usage: mobilenet_v3_large() and returns: _mobilenet_v3_model