def validate(
        self,
        model: AnomalibModule | None = None,
        dataloaders: EVAL_DATALOADERS | None = None,
        ckpt_path: str | Path | None = None,
        verbose: bool = True,
        datamodule: AnomalibDataModule | None = None,
    ) -> _EVALUATE_OUTPUT | None:
        """Validate the model using the trainer.

        Args:
            model (AnomalibModule | None, optional): Model to be validated.
                Defaults to None.
            dataloaders (EVAL_DATALOADERS | None, optional): Dataloaders to be used for
                validation.
                Defaults to None.
            ckpt_path (str | None, optional): Checkpoint path. If provided, the model will be loaded from this path.
                Defaults to None.
            verbose (bool, optional): Boolean to print the validation results.
                Defaults to True.
            datamodule (AnomalibDataModule | None, optional): A :class:`~lightning.pytorch.core.datamodule
                AnomalibDataModule` that defines the
                :class:`~lightning.pytorch.core.hooks.DataHooks.val_dataloader` hook.
                Defaults to None.

        Returns:
            _EVALUATE_OUTPUT | None: Validation results.

        CLI Usage:
            1. you can pick a model.
                ```python
                anomalib validate --model anomalib.models.Padim
                ```
            2. Of course, you can override the various values with commands.
                ```python
                anomalib validate --model anomalib.models.Padim --data <CONFIG | CLASS_PATH_OR_NAME>
                ```
            4. If you have a ready configuration file, run it like this.
                ```python
                anomalib validate --config <config_file_path>
                ```
        """
        if ckpt_path:
            ckpt_path = Path(ckpt_path).resolve()
        if model:
            self._setup_trainer(model)
        return self.trainer.validate(model, dataloaders, ckpt_path, verbose, datamodule)
Validate the model using the trainer.

        Args:
            model (AnomalibModule | None, optional): Model to be validated.
                Defaults to None.
            dataloaders (EVAL_DATALOADERS | None, optional): Dataloaders to be used for
                validation.
                Defaults to None.
            ckpt_path (str | None, optional): Checkpoint path. If provided, the model will be loaded from this path.
                Defaults to None.
            verbose (bool, optional): Boolean to print the validation results.
                Defaults to True.
            datamodule (AnomalibDataModule | None, optional): A :class:`~lightning.pytorch.core.datamodule
                AnomalibDataModule` that defines the
                :class:`~lightning.pytorch.core.hooks.DataHooks.val_dataloader` hook.
                Defaults to None.

        Returns:
            _EVALUATE_OUTPUT | None: Validation results.

        CLI Usage:
            1. you can pick a model.
                ```python
                anomalib validate --model anomalib.models.Padim
                ```
            2. Of course, you can override the various values with commands.
                ```python
                anomalib validate --model anomalib.models.Padim --data <CONFIG | CLASS_PATH_OR_NAME>
                ```
            4. If you have a ready configuration file, run it like this.
                ```python
                anomalib validate --config <config_file_path>
                ```
        
src/anomalib/engine/engine.py