docstr: Initializes an EfficientNet model, cuts it at the global average pooling layer and returns the output features.
docstr_tok: ["Initializes","an","EfficientNet","model",",","cuts","it","at","the","global","average","pooling","layer","and","returns","the","output","features","."]
code: def __init__(self) -> None:
      """
      Initializes an EfficientNet model, cuts it at the global average pooling layer and returns the output features.
      """
      super().__init__()
      self.effnet_b4 = models.efficientnet_b4(
          weights="EfficientNet_B4_Weights.IMAGENET1K_V1"
      ).eval()
  
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 EfficientNet object from inherited base classes: torch.nn with example: obj = EfficientNet()