def validation_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
        r"""Operates on a single batch of data from the validation set. In this step you'd might generate examples or
        calculate anything of interest like accuracy.

        Args:
            batch: The output of your data iterable, normally a :class:`~torch.utils.data.DataLoader`.
            batch_idx: The index of this batch.
            dataloader_idx: The index of the dataloader that produced this batch.
                (only if multiple dataloaders used)

        Return:
            - :class:`~torch.Tensor` - The loss tensor
            - ``dict`` - A dictionary. Can include any keys, but must include the key ``'loss'``.
            - ``None`` - Skip to the next batch.

        .. code-block:: python

            # if you have one val dataloader:
            def validation_step(self, batch, batch_idx): ...


            # if you have multiple val dataloaders:
            def validation_step(self, batch, batch_idx, dataloader_idx=0): ...

        Examples::

            # CASE 1: A single validation dataset
            def validation_step(self, batch, batch_idx):
                x, y = batch

                # implement your own
                out = self(x)
                loss = self.loss(out, y)

                # log 6 example images
                # or generated text... or whatever
                sample_imgs = x[:6]
                grid = torchvision.utils.make_grid(sample_imgs)
                self.logger.experiment.add_image('example_images', grid, 0)

                # calculate acc
                labels_hat = torch.argmax(out, dim=1)
                val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

                # log the outputs!
                self.log_dict({'val_loss': loss, 'val_acc': val_acc})

        If you pass in multiple val dataloaders, :meth:`validation_step` will have an additional argument. We recommend
        setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

        .. code-block:: python

            # CASE 2: multiple validation dataloaders
            def validation_step(self, batch, batch_idx, dataloader_idx=0):
                # dataloader_idx tells you which dataset this is.
                ...

        Note:
            If you don't need to validate you don't need to implement this method.

        Note:
            When the :meth:`validation_step` is called, the model has been put in eval mode
            and PyTorch gradients have been disabled. At the end of validation,
            the model goes back to training mode and gradients are enabled.

        """
Operates on a single batch of data from the validation set. In this step you'd might generate examples or
        calculate anything of interest like accuracy.

        Args:
            batch: The output of your data iterable, normally a :class:`~torch.utils.data.DataLoader`.
            batch_idx: The index of this batch.
            dataloader_idx: The index of the dataloader that produced this batch.
                (only if multiple dataloaders used)

        Return:
            - :class:`~torch.Tensor` - The loss tensor
            - ``dict`` - A dictionary. Can include any keys, but must include the key ``'loss'``.
            - ``None`` - Skip to the next batch.

        .. code-block:: python

            # if you have one val dataloader:
            def validation_step(self, batch, batch_idx): ...


            # if you have multiple val dataloaders:
            def validation_step(self, batch, batch_idx, dataloader_idx=0): ...

        Examples::

            # CASE 1: A single validation dataset
            def validation_step(self, batch, batch_idx):
                x, y = batch

                # implement your own
                out = self(x)
                loss = self.loss(out, y)

                # log 6 example images
                # or generated text... or whatever
                sample_imgs = x[:6]
                grid = torchvision.utils.make_grid(sample_imgs)
                self.logger.experiment.add_image('example_images', grid, 0)

                # calculate acc
                labels_hat = torch.argmax(out, dim=1)
                val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

                # log the outputs!
                self.log_dict({'val_loss': loss, 'val_acc': val_acc})

        If you pass in multiple val dataloaders, :meth:`validation_step` will have an additional argument. We recommend
        setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

        .. code-block:: python

            # CASE 2: multiple validation dataloaders
            def validation_step(self, batch, batch_idx, dataloader_idx=0):
                # dataloader_idx tells you which dataset this is.
                ...

        Note:
            If you don't need to validate you don't need to implement this method.

        Note:
            When the :meth:`validation_step` is called, the model has been put in eval mode
            and PyTorch gradients have been disabled. At the end of validation,
            the model goes back to training mode and gradients are enabled.

        
src/lightning/pytorch/core/module.py