class_docstr: A dummy model which calls visualizer callback on fake images and masks.
code: def __init__(self, hparams: Union[DictConfig, ListConfig]):
      super().__init__()
      self.model = _DummyModel()
      self.task = "segmentation"
      self.mode = "full"
      self.callbacks = [
          ImageVisualizerCallback(
              task=self.task,
              mode=self.mode,
              image_save_path=hparams.project.path + "/images",
              log_images=True,
              save_images=True,
          )
      ]  # test if this is removed
  
      self.image_metrics, self.pixel_metrics = get_metrics(hparams)
      self.image_metrics.set_threshold(
          hparams.model.threshold.image_default
      )
      self.pixel_metrics.set_threshold(
          hparams.model.threshold.pixel_default
      )
  
imports: from pathlib import Path
  from typing import Union
  import torch
  from omegaconf.dictconfig import DictConfig
  from omegaconf.listconfig import ListConfig
  from torch import nn
  from anomalib.models.components import AnomalyModule
  from anomalib.utils.callbacks import ImageVisualizerCallback
  from tests.helpers.dataset import get_dataset_path
  from tests.helpers.metrics import get_metrics
label_desc: Use image node_id 1 to create a new DummyModule object from inherited base classes: AnomalyModule with example: obj = DummyModule(hparams)