class_docstr: PL Lightning Module for the GANomaly Algorithm.
  
  Args:
      batch_size (int): Batch size.
      input_size (tuple[int, int]): Input dimension.
      n_features (int): Number of features layers in the CNNs.
      latent_vec_size (int): Size of autoencoder latent vector.
      extra_layers (int, optional): Number of extra layers for encoder/decoder. Defaults to 0.
      add_final_conv_layer (bool, optional): Add convolution layer at the end. Defaults to True.
      wadv (int, optional): Weight for adversarial loss. Defaults to 1.
      wcon (int, optional): Image regeneration weight. Defaults to 50.
      wenc (int, optional): Latent vector encoder weight. Defaults to 1.
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docstr: Reset min and max values for current validation epoch.
docstr_tok: ["Reset","min","and","max","values","for","current","validation","epoch","."]
code: def on_validation_start(self) -> None:
      """Reset min and max values for current validation epoch."""
      self._reset_min_max()
      return super().on_validation_start()
  
imports: from __future__ import annotations
  import logging
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.callbacks import Callback, EarlyStopping
  from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
  from torch import Tensor, optim
  from anomalib.models.components import AnomalyModule
  from anomalib.models.ganomaly.loss import DiscriminatorLoss, GeneratorLoss
  from .torch_model import GanomalyModel
label_desc: Use image node_id 5 for calling the Ganomaly obj's underlying member method code with example usage: obj.on_validation_start() and returns: super