class_docstr: PL Lightning Module for the EfficientAd Algorithm.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["PL","Lightning","Module","for","the","EfficientAd","Algorithm",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","params"]
code: def __init__(self, hparams: DictConfig | ListConfig) -> None:
      super().__init__(
          teacher_out_channels=hparams.model.teacher_out_channels,
          model_size=hparams.model.model_size,
          lr=hparams.model.lr,
          weight_decay=hparams.model.weight_decay,
          padding=hparams.model.padding,
          pad_maps=hparams.model.pad_maps,
          image_size=hparams.dataset.image_size,
          batch_size=hparams.dataset.train_batch_size,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
imports: from __future__ import annotations
  import logging
  from pathlib import Path
  import albumentations
  import numpy
  import torch
  import tqdm
  from albumentations.pytorch import ToTensorV2
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from torch import Tensor, optim
  from torch.utils.data import DataLoader
  from torchvision.datasets import ImageFolder
  from anomalib.data.utils import DownloadInfo, download_and_extract
  from anomalib.models.components import AnomalyModule
  from .torch_model import EfficientAdModel, EfficientAdModelSize, reduce_tensor_elems
label_desc: Use image node_id 1 to create a new EfficientAdLightning object from inherited base classes: EfficientAd with example: obj = EfficientAdLightning(hparams)