docstr: Initialize a mobilenetv3 model, cuts it at the global average pooling layer and returns the output features.
docstr_tok: ["Initialize","a","mobilenetv3","model",",","cuts","it","at","the","global","average","pooling","layer","and","returns","the","output","features","."]
code: def __init__(self) -> None:
      """
      Initialize a mobilenetv3 model, cuts it at the global average pooling layer and returns the output features.
      """
      super().__init__()
      mobilenet = models.mobilenet_v3_small(
          weights="MobileNet_V3_Small_Weights.IMAGENET1K_V1"
      ).eval()
      self.mobilenet_gap_op = torch.nn.Sequential(
          mobilenet.features, mobilenet.avgpool
      )
  
imports: from PIL.Image import Image
  from typing import Callable, NamedTuple, Optional
  import torch
  import torch.nn
  from torchvision import models
  from torchvision.transforms import transforms
  from torchvision.models import vit_b_16, EfficientNet_B4_Weights
  from torchvision.models.vision_transformer import ViT_B_16_Weights
label_desc: Use image node_id 1 to create a new MobilenetV3 object from inherited base classes: torch.nn with example: obj = MobilenetV3()