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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def train_step_single(self, Xi, yi, **fit_params):
 """Compute y_pred, loss value, and update net's gradients. The module is set to be in train mode (e.g. dropout is applied). Parameters Xi : input data A batch of the input data. yi : target data A batch of the target data. **fit_params : dict Additional parameters passed to the ``forward`` method of the module and to the ``self.train_split`` call. """

        self.module_.train()
        self.optimizer_.zero_grad()
        y_pred = self.infer(Xi, **fit_params)
        loss = self.get_loss(y_pred, yi, X=Xi, training=True)
        loss.backward()

        self.notify(
            'on_grad_computed',
            named_parameters=TeeGenerator(self.module_.named_parameters()),
            X=Xi,
            y=yi
        )

        return {
            'loss': loss,
            'y_pred': y_pred,
            }