class_docstr: Rkde: Deep Feature Kernel Density Estimation.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["Rkde",":","Deep","Feature","Kernel","Density","Estimation",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","params"]
code: def __init__(self, hparams: DictConfig | ListConfig) -> None:
      super().__init__(
          roi_stage=RoiStage(hparams.model.roi_stage),
          roi_score_threshold=hparams.model.roi_score_threshold,
          min_box_size=hparams.model.min_box_size,
          iou_threshold=hparams.model.iou_threshold,
          max_detections_per_image=hparams.model.max_detections_per_image,
          n_pca_components=hparams.model.n_pca_components,
          feature_scaling_method=FeatureScalingMethod(
              hparams.model.feature_scaling_method
          ),
          max_training_points=hparams.model.max_training_points,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
file_path: anomalib/src/anomalib/models/rkde/lightning_model.py
imports: from __future__ import annotations
  import logging
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from torch import Tensor
  from anomalib.models.components import AnomalyModule
  from anomalib.models.components.classification import FeatureScalingMethod
  from .region_extractor import RoiStage
  from .torch_model import RkdeModel
label_desc: Use image node_id 1 to create a new RkdeLightning object from inherited base classes: Rkde with example: obj = RkdeLightning(hparams)