class_docstr: DSR: A Dual Subspace Re-Projection Network for Surface Anomaly Detection
  
  Args:
      latent_anomaly_strength (float, optional): Strength of the generated anomalies in the latent space.
code: def prepare_pretrained_model(self) -> Path:
      pretrained_models_dir = Path("./pre_trained/")
      if not (
          pretrained_models_dir / "vq_model_pretrained_128_4096.pckl"
      ).is_file():
          download_and_extract(
              pretrained_models_dir, WEIGHTS_DOWNLOAD_INFO
          )
      return pretrained_models_dir / "vq_model_pretrained_128_4096.pckl"
  
imports: from __future__ import annotations
  import logging
  from pathlib import Path
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from torch import Tensor
  from anomalib.data.utils import DownloadInfo, download_and_extract
  from anomalib.data.utils.augmenter import Augmenter
  from anomalib.models.components import AnomalyModule
  from anomalib.models.dsr.anomaly_generator import DsrAnomalyGenerator
  from anomalib.models.dsr.loss import DsrSecondStageLoss, DsrThirdStageLoss
  from anomalib.models.dsr.torch_model import DsrModel
label_desc: Use image node_id 2 for calling the Dsr obj's underlying member method code with example usage: obj.prepare_pretrained_model() and returns: unknown