# Buggy code
def plot_interpretation(
    self,
    x: Dict[str, torch.Tensor],
    output: Dict[str, torch.Tensor],
    idx: int,
    ax=None,
    plot_seasonality_and_generic_on_secondary_axis: bool = False,
) -> plt.Figure:
    """
    Plot interpretation.

    Plot two pannels: prediction and backcast vs actuals and
    decomposition of prediction into trend, seasonality and generic forecast.

    Args:
        x (Dict[str, torch.Tensor]): network input
        output (Dict[str, torch.Tensor]): network output
        idx (int): index of sample for which to plot the interpretation.
        ax (List[matplotlib axes], optional): list of two matplotlib axes onto which to plot the interpretation.
            Defaults to None.
        plot_seasonality_and_generic_on_secondary_axis (bool, optional): if to plot seasonality and
            generic forecast on secondary axis in second panel. Defaults to False.

    Returns:
        plt.Figure: matplotlib figure
    """
    if ax is None:
        fig, ax = plt.subplots(2, 1, figsize=(6, 8))
    else:
        fig = ax.get_figure()

    time = torch.arange(-self.hparams.context_length, self.hparams.prediction_length)

    def to_prediction(y):
        return self.transform_output(
            dict(prediction=y[[idx]], target_scale=x["target_scale"][[idx]])
        )[0]

    # plot target vs prediction
    ax[0].plot(
        time,
        torch.cat([x["encoder_target"][idx], x["decoder_target"][idx]]),
        label="target",
    )
    ax[0].plot(
        time,
        torch.cat(
            [
                to_prediction(output["backcast"].detach()),
                output["prediction"][idx].detach(),
            ],
            dim=0,
        ),
        label="prediction",
    )
    ax[0].set_xlabel("Time")

    # plot blocks
    prop_cycle = iter(plt.rcParams["axes.prop_cycle"])
    next(prop_cycle)  # prediction
    next(prop_cycle)  # observations
    if plot_seasonality_and_generic_on_secondary_axis:
        ax2 = ax[1].twinx()
        ax2.set_ylabel("Seasonality / Generic")
    else:
        ax2 = ax[1]
    for title in ["trend", "seasonality", "generic"]:
        if title not in self.hparams.stack_types:
            continue
        if title == "trend":
            ax[1].plot(
                time,
                to_prediction(output[title]),
                label=title.capitalize(),
                c=next(prop_cycle)["color"],
            )
        else:
            ax2.plot(
                time,
                to_prediction(output[title]),
                label=title.capitalize(),
                c=next(prop_cycle)["color"],
            )
    ax[1].set_xlabel("Time")
    ax[1].set_ylabel("Decomposition")

    fig.legend()
    return fig


# Traceback (RuntimeError)
RuntimeError                              Traceback (most recent call last)
<ipython-input-11-a92b5627800b> in <module>
5     val_dataloaders=val_dataloader,
6     max_lr=10.0,
----> 7     min_lr=1e-6,
8 )
9

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/tuner/tuning.py in lr_find(self, model, train_dataloader, val_dataloaders, min_lr, max_lr, num_training, mode, early_stop_threshold, datamodule)
128             mode,
129             early_stop_threshold,
--> 130             datamodule,
131         )
132

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/tuner/lr_finder.py in lr_find(trainer, model, train_dataloader, val_dataloaders, min_lr, max_lr, num_training, mode, early_stop_threshold, datamodule)
173                 train_dataloader=train_dataloader,
174                 val_dataloaders=val_dataloaders,
--> 175                 datamodule=datamodule)
176
177     # Prompt if we stopped early

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
437         self.call_hook('on_fit_start')
438
--> 439         results = self.accelerator_backend.train()
440         self.accelerator_backend.teardown()
441

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in train(self)
52
53         # train or test
---> 54         results = self.train_or_test()
55         return results
56

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in train_or_test(self)
64             results = self.trainer.run_test()
65         else:
---> 66             results = self.trainer.train()
67         return results
68

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in train(self)
459
460     def train(self):
--> 461         self.run_sanity_check(self.get_model())
462
463         # enable train mode

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_sanity_check(self, ref_model)
645
646             # run eval step
--> 647             _, eval_results = self.run_evaluation(test_mode=False, max_batches=self.num_sanity_val_batches)
648
649             # allow no returns from eval

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_evaluation(self, test_mode, max_batches)
565
566                 # lightning module methods
--> 567                 output = self.evaluation_loop.evaluation_step(test_mode, batch, batch_idx, dataloader_idx)
568                 output = self.evaluation_loop.evaluation_step_end(output)
569

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py in evaluation_step(self, test_mode, batch, batch_idx, dataloader_idx)
169             output = self.trainer.accelerator_backend.test_step(args)
170         else:
--> 171             output = self.trainer.accelerator_backend.validation_step(args)
172
173         # track batch size for weighted average

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in validation_step(self, args)
76                 output = self.__validation_step(args)
77         else:
---> 78             output = self.__validation_step(args)
79
80         return output

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in __validation_step(self, args)
84         batch = self.to_device(batch)
85         args[0] = batch
---> 86         output = self.trainer.model.validation_step(*args)
87         return output
88

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_forecasting/models/base_model.py in validation_step(self, batch, batch_idx)
138     def validation_step(self, batch, batch_idx):
139         x, y = batch
--> 140         log, _ = self.step(x, y, batch_idx, label="val")  # log loss
141         self.log("val_loss", log["loss"], on_step=False, on_epoch=True, prog_bar=True)
142         return log

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py in step(self, x, y, batch_idx, label)
566         """
567         # extract data and run model
--> 568         log, out = super().step(x, y, batch_idx, label=label)
569         # calculate interpretations etc for latter logging
570         if self.log_interval(label == "train") > 0:

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_forecasting/models/base_model.py in step(self, x, y, batch_idx, label)
194             loss = loss * (1 + monotinicity_loss)
195         else:
--> 196             out = self(x)
197             out["prediction"] = self.transform_output(out)
198

~/repo/emart-promo/env/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725             result = self._slow_forward(*input, **kwargs)
726         else:
--> 727             result = self.forward(*input, **kwargs)
728         for hook in itertools.chain(
729                 _global_forward_hooks.values(),

~/repo/emart-promo/env/lib/python3.7/site-packages/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py in forward(self, x)
489         encoder_output, (hidden, cell) = self.lstm_encoder(
490             rnn.pack_padded_sequence(
--> 491                 embeddings_varying_encoder, lstm_encoder_lengths, enforce_sorted=False, batch_first=True
492             ),
493             (input_hidden, input_cell),

~/repo/emart-promo/env/lib/python3.7/site-packages/torch/nn/utils/rnn.py in pack_padded_sequence(input, lengths, batch_first, enforce_sorted)
242
243     data, batch_sizes = \
--> 244         _VF._pack_padded_sequence(input, lengths, batch_first)
245     return _packed_sequence_init(data, batch_sizes, sorted_indices, None)
246

RuntimeError: 'lengths' argument should be a 1D CPU int64 tensor, but got 1D cuda:0 Long tensor

# Fixed code
def plot_interpretation(
    self,
    x: Dict[str, torch.Tensor],
    output: Dict[str, torch.Tensor],
    idx: int,
    ax=None,
    plot_seasonality_and_generic_on_secondary_axis: bool = False,
) -> plt.Figure:
    """
    Plot interpretation.

    Plot two pannels: prediction and backcast vs actuals and
    decomposition of prediction into trend, seasonality and generic forecast.

    Args:
        x (Dict[str, torch.Tensor]): network input
        output (Dict[str, torch.Tensor]): network output
        idx (int): index of sample for which to plot the interpretation.
        ax (List[matplotlib axes], optional): list of two matplotlib axes onto which to plot the interpretation.
            Defaults to None.
        plot_seasonality_and_generic_on_secondary_axis (bool, optional): if to plot seasonality and
            generic forecast on secondary axis in second panel. Defaults to False.

    Returns:
        plt.Figure: matplotlib figure
    """
    if ax is None:
        fig, ax = plt.subplots(2, 1, figsize=(6, 8))
    else:
        fig = ax.get_figure()

    time = torch.arange(-self.hparams.context_length, self.hparams.prediction_length)

    def to_prediction(y):
        return self.transform_output(
            dict(prediction=y[[idx]], target_scale=x["target_scale"][[idx]])
        )[0]

    # plot target vs prediction
    ax[0].plot(
        time,
        torch.cat([x["encoder_target"][idx], x["decoder_target"][idx]]).cpu(),
        label="target",
    )
    ax[0].plot(
        time,
        torch.cat(
            [
                to_prediction(output["backcast"].detach()),
                output["prediction"][idx].detach(),
            ],
            dim=0,
        ).cpu(),
        label="prediction",
    )
    ax[0].set_xlabel("Time")

    # plot blocks
    prop_cycle = iter(plt.rcParams["axes.prop_cycle"])
    next(prop_cycle)  # prediction
    next(prop_cycle)  # observations
    if plot_seasonality_and_generic_on_secondary_axis:
        ax2 = ax[1].twinx()
        ax2.set_ylabel("Seasonality / Generic")
    else:
        ax2 = ax[1]
    for title in ["trend", "seasonality", "generic"]:
        if title not in self.hparams.stack_types:
            continue
        if title == "trend":
            ax[1].plot(
                time,
                to_prediction(output[title]).cpu(),
                label=title.capitalize(),
                c=next(prop_cycle)["color"],
            )
        else:
            ax2.plot(
                time,
                to_prediction(output[title]).cpu(),
                label=title.capitalize(),
                c=next(prop_cycle)["color"],
            )
    ax[1].set_xlabel("Time")
    ax[1].set_ylabel("Decomposition")

    fig.legend()
    return fig