class_docstr: AI-VAD: Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["AI-VAD",":","Attribute-based","Representations","for","Accurate","and","Interpretable","Video","Anomaly","Detection",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","params"]
code: def __init__(self, hparams: DictConfig | ListConfig) -> None:
      super().__init__(
          box_score_thresh=hparams.model.box_score_thresh,
          persons_only=hparams.model.persons_only,
          min_bbox_area=hparams.model.min_bbox_area,
          max_bbox_overlap=hparams.model.max_bbox_overlap,
          enable_foreground_detections=hparams.model.enable_foreground_detections,
          foreground_kernel_size=hparams.model.foreground_kernel_size,
          foreground_binary_threshold=hparams.model.foreground_binary_threshold,
          n_velocity_bins=hparams.model.n_velocity_bins,
          use_velocity_features=hparams.model.use_velocity_features,
          use_pose_features=hparams.model.use_pose_features,
          use_deep_features=hparams.model.use_deep_features,
          n_components_velocity=hparams.model.n_components_velocity,
          n_neighbors_pose=hparams.model.n_neighbors_pose,
          n_neighbors_deep=hparams.model.n_neighbors_deep,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
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 1 to create a new AiVadLightning object from inherited base classes: AiVad with example: obj = AiVadLightning(hparams)