class_docstr: DFM: Deep Featured Kernel Density Estimation.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["DFM",":","Deep","Featured","Kernel","Density","Estimation",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","params"]
code: def __init__(self, hparams: DictConfig | ListConfig) -> None:
      super().__init__(
          input_size=hparams.model.input_size,
          backbone=hparams.model.backbone,
          layer=hparams.model.layer,
          pre_trained=hparams.model.pre_trained,
          pooling_kernel_size=hparams.model.pooling_kernel_size,
          pca_level=hparams.model.pca_level,
          score_type=hparams.model.score_type,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
file_path: anomalib/src/anomalib/models/dfm/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 .torch_model import DFMModel
label_desc: Use image node_id 1 to create a new DfmLightning object from inherited base classes: Dfm with example: obj = DfmLightning(hparams)