class_docstr: PatchcoreLightning Module to train PatchCore algorithm.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["PatchcoreLightning","Module","to","train","PatchCore","algorithm",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","params"]
code: def __init__(self, hparams) -> None:
      super().__init__(
          input_size=hparams.model.input_size,
          backbone=hparams.model.backbone,
          layers=hparams.model.layers,
          pre_trained=hparams.model.pre_trained,
          coreset_sampling_ratio=hparams.model.coreset_sampling_ratio,
          num_neighbors=hparams.model.num_neighbors,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
file_path: anomalib/src/anomalib/models/patchcore/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.patchcore.torch_model import PatchcoreModel
label_desc: Use image node_id 1 to create a new PatchcoreLightning object from inherited base classes: Patchcore with example: obj = PatchcoreLightning(hparams)