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 90% and 99.5% quantiles of the given anomaly maps.
  
  If the total number of elements in the given maps is larger than 16777216
  the returned quantiles are computed on a random subset of the given
  elements.
  
  Args:
      maps (list[Tensor]): List of anomaly maps.
  
  Returns:
      tuple[Tensor, Tensor]: Two scalars - the 90% and the 99.5% quantile.
docstr_tok: ["Calculate","90","%","and","99.5","%","quantiles","of","the","given","anomaly","maps",".","If","the","total","number","of","elements","in","the","given","maps","is","larger","than","16777216","the","returned","quantiles","are","computed","on","a","random","subset","of","the","given","elements",".","Args",":","maps","(","list","[","Tensor","]",")",":","List","of","anomaly","maps",".","Returns",":","tuple","[","Tensor",",","Tensor","]",":","Two","scalars","-","the","90","%","and","the","99.5","%","quantile","."]
code: def _get_quantiles_of_maps(
      self, maps: list[Tensor]
  ) -> tuple[Tensor, Tensor]:
      """Calculate 90% and 99.5% quantiles of the given anomaly maps.
  
      If the total number of elements in the given maps is larger than 16777216
      the returned quantiles are computed on a random subset of the given
      elements.
  
      Args:
          maps (list[Tensor]): List of anomaly maps.
  
      Returns:
          tuple[Tensor, Tensor]: Two scalars - the 90% and the 99.5% quantile.
      """
  
      maps_flat = reduce_tensor_elems(torch.cat(maps))
      qa = torch.quantile(maps_flat, q=0.9).to(self.device)
      qb = torch.quantile(maps_flat, q=0.995).to(self.device)
      return qa, qb
  
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 6 for calling the EfficientAd obj's underlying member method code with example usage: obj._get_quantiles_of_maps(maps) and returns: qa,  qb