class_docstr: EfficientAd model.
  
  Args:
      teacher_out_channels (int): number of convolution output channels of the pre-trained teacher model
      pretrained_models_dir (str): path to the pretrained model weights
      input_size (tuple): size of input images
      model_size (str): size of student and teacher model
      padding (bool): use padding in convoluional layers
      pad_maps (bool): relevant if padding is set to False. In this case, pad_maps = True pads the
          output anomaly maps so that their size matches the size in the padding = True case.
      device (str): which device the model should be loaded on
code: def choose_random_aug_image(self, image: Tensor) -> Tensor:
      transform_functions = [
          transforms.functional.adjust_brightness,
          transforms.functional.adjust_contrast,
          transforms.functional.adjust_saturation,
      ]
      # Sample an augmentation coefficient λ from the uniform distribution U(0.8, 1.2)
      coefficient = random.uniform(0.8, 1.2)  # nosec: B311
      transform_function = random.choice(
          transform_functions
      )  # nosec: B311
      return transform_function(image, coefficient)
  
imports: from __future__ import annotations
  import logging
  import random
  from enum import Enum
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from torchvision import transforms
label_desc: Use image node_id 3 for calling the EfficientAdModel obj's underlying member method code with example usage: obj.choose_random_aug_image(image) and returns: transform_function