Repository: Lightning-AI/pytorch-lightning
File: pl_examples/bug_report_model.py (Python)

Code before:

        Use as follows:
        - subclass
        - modify the behavior for what you want

        class TestModel(BaseTestModel):
            def training_step(...):
                # do your own thing

        or:

        model = BaseTestModel()
        model.training_epoch_end = None

        """
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def loss(self, batch, prediction):
        # An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
        return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))

    def _step(self, x):
        x = self.layer(x)
        out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
        return out

    def training_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"loss": loss}

    def training_step_end(self, training_step_outputs):
        return training_step_outputs

    def training_epoch_end(self, outputs) -> None:
        torch.stack([x["loss"] for x in outputs]).mean()

    def validation_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"x": loss}

    def validation_epoch_end(self, outputs) -> None:
        torch.stack([x['x'] for x in outputs]).mean()

    def test_step(self, batch, batch_idx):
        output = self.layer(batch)

Review comment (suggestion):
This has to be called somewhere. So the reference there Must ne changed as well I think :)
```suggestion
    def step(self, x):
```

Code after:
        """
        Testing PL Module

        Use as follows:
        - subclass
        - modify the behavior for what you want

        class TestModel(BaseTestModel):
            def training_step(...):
                # do your own thing

        or:

        model = BaseTestModel()
        model.training_epoch_end = None

        """
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def loss(self, batch, prediction):
        # An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
        return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))

    def step(self, x):
        x = self.layer(x)
        out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
        return out

    def training_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"loss": loss}

    def training_step_end(self, training_step_outputs):
        return training_step_outputs

    def training_epoch_end(self, outputs) -> None:
        torch.stack([x["loss"] for x in outputs]).mean()

    def validation_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"x": loss}

    def validation_epoch_end(self, outputs) -> None:
        torch.stack([x['x'] for x in outputs]).mean()