class_docstr: DFKDE: Deep Feature Kernel Density Estimation.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["DFKDE",":","Deep","Feature","Kernel","Density","Estimation",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","params"]
code: def __init__(self, hparams: DictConfig | ListConfig) -> None:
      super().__init__(
          layers=hparams.model.layers,
          backbone=hparams.model.backbone,
          pre_trained=hparams.model.pre_trained,
          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/dfkde/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 .torch_model import DfkdeModel
label_desc: Use image node_id 1 to create a new DfkdeLightning object from inherited base classes: Dfkde with example: obj = DfkdeLightning(hparams)