class_docstr: PL Lightning Module for the EfficientAd algorithm.
  
  Args:
      teacher_file_name (str): path to the pre-trained teacher model
      teacher_out_channels (int): number of convolution output channels
      image_size (tuple): size of input images
      model_size (str): size of student and teacher model
      lr (float): learning rate
      weight_decay (float): optimizer weight decay
      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.
      batch_size (int): batch size for imagenet dataloader
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docstr: Training step for EfficientAd returns the student, autoencoder and combined loss.
  
  Args:
      batch (batch: dict[str, str | Tensor]): Batch containing image filename, image, label and mask
  
  Returns:
    Loss.
docstr_tok: ["Training","step","for","EfficientAd","returns","the","student",",","autoencoder","and","combined","loss",".","Args",":","batch","(","batch",":","dict","[","str",",","str","|","Tensor","]",")",":","Batch","containing","image","filename",",","image",",","label","and","mask","Returns",":","Loss","."]
code: def training_step(
      self, batch: dict[str, str | Tensor], *args, **kwargs
  ) -> dict[str, Tensor]:
      """Training step for EfficientAd returns the student, autoencoder and combined loss.
  
      Args:
          batch (batch: dict[str, str | Tensor]): Batch containing image filename, image, label and mask
  
      Returns:
        Loss.
      """
      del args, kwargs  # These variables are not used.
  
      try:
          # infinite dataloader; [0] getting the image not the label
          batch_imagenet = next(self.imagenet_iterator)[0]["image"].to(
              self.device
          )
      except StopIteration:
          self.imagenet_iterator = iter(self.imagenet_loader)
          batch_imagenet = next(self.imagenet_iterator)[0]["image"].to(
              self.device
          )
  
      loss_st, loss_ae, loss_stae = self.model(
          batch=batch["image"], batch_imagenet=batch_imagenet
      )
  
      loss = loss_st + loss_ae + loss_stae
      self.log(
          "train_st",
          loss_st.item(),
          on_epoch=True,
          prog_bar=True,
          logger=True,
      )
      self.log(
          "train_ae",
          loss_ae.item(),
          on_epoch=True,
          prog_bar=True,
          logger=True,
      )
      self.log(
          "train_stae",
          loss_stae.item(),
          on_epoch=True,
          prog_bar=True,
          logger=True,
      )
      self.log(
          "train_loss",
          loss.item(),
          on_epoch=True,
          prog_bar=True,
          logger=True,
      )
      return {"loss": loss}
  
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 9 for calling the EfficientAd obj's underlying member method code with example usage: obj.training_step(batch) and returns: dict