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 is_set(self, p_dic: nn.ParameterDict) -> bool:
      for _, value in p_dic.items():
          if value.sum() != 0:
              return True
      return False
  
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 2 for calling the EfficientAdModel obj's underlying member method code with example usage: obj.is_set(p_dic) and returns: False, True