code: def mobilenet_v3_large(
      pretrained: bool = False,
      include_top: bool = False,
      freeze: bool = False,
  ):
      """
      Create an instance of mobilenet_v3_large model.
  
      :param pretrained: (bool) Whether to return a model pre-trained on ImageNet. Defaults to False.
      :param include_top: (bool) Whether to include the fully-connected layer at the top of the
             network. If False, it will be replaced by an identity layer, and the output size
             of the returned model can be obtained by `model.get_output_size()`. Defaults to False.
      :param freeze: (bool) Whether to freeze the model besides the top layer of the network (only
             enable training on the top layer). If `include_top` is False and `freeze` is True,
             then all layers will be frozen in the model to be returned. Defaults to False.
      :return: PyTorch model mobilenet_v3_large.
      """
      model = torchvision.models.mobilenet_v3_large(pretrained)
      if freeze:
          set_parameter_requires_grad(model, "classifier")
      if not include_top:
          output_size = model.classifier[0].in_features
          model.classifier = nn.Identity()
          return BackboneModule(model, output_size)
      else:
          return model
  
file_path: BigDL/python/nano/src/bigdl/nano/pytorch/vision/models/vision.py
imports: import torchvision
  from ._utils import *
label_desc: Use image node_id 4 for calling a global function with example usage: mobilenet_v3_large(pretrained, include_top, freeze) and returns: BackboneModule, model