class_docstr: AI-VAD: Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection.
  
  Args:
      box_score_thresh (float): Confidence threshold for bounding box predictions.
      persons_only (bool): When enabled, only regions labeled as person are included.
      min_bbox_area (int): Minimum bounding box area. Regions with a surface area lower than this value are excluded.
      max_bbox_overlap (float): Maximum allowed overlap between bounding boxes.
      enable_foreground_detections (bool): Add additional foreground detections based on pixel difference between
          consecutive frames.
      foreground_kernel_size (int): Gaussian kernel size used in foreground detection.
      foreground_binary_threshold (int): Value between 0 and 255 which acts as binary threshold in foreground
          detection.
      n_velocity_bins (int): Number of discrete bins used for velocity histogram features.
      use_velocity_features (bool): Flag indicating if velocity features should be used.
      use_pose_features (bool): Flag indicating if pose features should be used.
      use_deep_features (bool): Flag indicating if deep features should be used.
      n_components_velocity (int): Number of components used by GMM density estimation for velocity features.
      n_neighbors_pose (int): Number of neighbors used in KNN density estimation for pose features.
      n_neighbors_deep (int): Number of neighbors used in KNN density estimation for deep features.
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docstr: AI-VAD training does not involve fine-tuning of NN weights, no optimizers needed.
docstr_tok: ["AI-VAD","training","does","not","involve","fine-tuning","of","NN","weights",",","no","optimizers","needed","."]
code: def configure_optimizers() -> None:
      """AI-VAD training does not involve fine-tuning of NN weights, no optimizers needed."""
      return None
  
file_path: anomalib/src/anomalib/models/ai_vad/lightning_model.py
imports: from __future__ import annotations
  import logging
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from torch import Tensor
  from anomalib.models.ai_vad.torch_model import AiVadModel
  from anomalib.models.components import AnomalyModule
label_desc: Use image node_id 2 for calling the AiVad obj's underlying member method code with example usage: obj.configure_optimizers() and returns: None