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
class_docstr_tok: ["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"]
docstr: Calculate or load the channel-wise mean and std of the training dataset and push to the model.
docstr_tok: ["Calculate","or","load","the","channel-wise","mean","and","std","of","the","training","dataset","and","push","to","the","model","."]
code: def on_train_start(self) -> None:
      """Calculate or load the channel-wise mean and std of the training dataset and push to the model."""
      if not self.model.is_set(self.model.mean_std):
          channel_mean_std = self.teacher_channel_mean_std(
              self.trainer.datamodule.train_dataloader()
          )
          self.model.mean_std.update(channel_mean_std)
  
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 8 for calling the EfficientAd obj's underlying member method code with example usage: obj.on_train_start() without return types