class_docstr: DFKDE: Deep Feature Kernel Density Estimation.
  
  Args:
      backbone (str): Pre-trained model backbone.
      pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
      max_training_points (int, optional): Number of training points to fit the KDE model.
          Defaults to 40000.
      pre_processing (str, optional): Preprocess features before passing to KDE.
          Options are between `norm` and `scale`. Defaults to "scale".
      n_components (int, optional): Number of PCA components. Defaults to 16.
      threshold_steepness (float, optional): Controls how quickly the value saturates around zero.
          Defaults to 0.05.
      threshold_offset (float, optional): Offset of the density function from 0. Defaults to 12.0.
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docstr: DFKDE doesn't require optimization, therefore returns no optimizers.
docstr_tok: ["DFKDE","does","n't","require","optimization",",","therefore","returns","no","optimizers","."]
code: def configure_optimizers() -> (
      None
  ):  # pylint: disable=arguments-differ
      """DFKDE doesn't require optimization, therefore returns no optimizers."""
      return None
  
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 2 for calling the Dfkde obj's underlying member method code with example usage: obj.configure_optimizers() and returns: None