class_docstr: DFM: Deep Featured Kernel Density Estimation.
  
  Args:
      backbone (str): Backbone CNN network
      layer (str): Layer to extract features from the backbone CNN
      input_size (tuple[int, int]): Input size for the model.
      pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
      pooling_kernel_size (int, optional): Kernel size to pool features extracted from the CNN.
          Defaults to 4.
      pca_level (float, optional): Ratio from which number of components for PCA are calculated.
          Defaults to 0.97.
      score_type (str, optional): Scoring type. Options are `fre` and `nll`. Defaults to "fre".
      nll: for Gaussian modeling, fre: pca feature-reconstruction error. Anomaly segmentation is
      supported with `fre` only. If using `nll`, set `task` in config.yaml to classification
class_docstr_tok: ["DFM",":","Deep","Featured","Kernel","Density","Estimation",".","Args",":","backbone","(","str",")",":","Backbone","CNN","network","layer","(","str",")",":","Layer","to","extract","features","from","the","backbone","CNN","input_size","(","tuple","[","int",",","int","]",")",":","Input","size","for","the","model",".","pre_trained","(","bool",",","optional",")",":","Boolean","to","check","whether","to","use","a","pre_trained","backbone",".","pooling_kernel_size","(","int",",","optional",")",":","Kernel","size","to","pool","features","extracted","from","the","CNN",".","Defaults","to","4.","pca_level","(","float",",","optional",")",":","Ratio","from","which","number","of","components","for","PCA","are","calculated",".","Defaults","to","0.97.","score_type","(","str",",","optional",")",":","Scoring","type",".","Options","are","`","fre","`","and","`","nll","`",".","Defaults","to","``","fre","''",".","nll",":","for","Gaussian","modeling",",","fre",":","pca","feature-reconstruction","error",".","Anomaly","segmentation","is","supported","with","`","fre","`","only",".","If","using","`","nll","`",",","set","`","task","`","in","config.yaml","to","classification"]
docstr: DFM doesn't require optimization, therefore returns no optimizers.
docstr_tok: ["DFM","does","n't","require","optimization",",","therefore","returns","no","optimizers","."]
code: def configure_optimizers() -> (
      None
  ):  # pylint: disable=arguments-differ
      """DFM doesn't require optimization, therefore returns no optimizers."""
      return None
  
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 2 for calling the Dfm obj's underlying member method code with example usage: obj.configure_optimizers() and returns: None