# Buggy code
def fit(self, y: pd.Series, X: pd.DataFrame):
    """
    Determine scales for each group

    Args:
        y (pd.Series): input data
        X (pd.DataFrame): dataframe with columns for each group defined in ``groups`` parameter.

    Returns:
        self
    """
    y = self._preprocess_y(y)
    if len(self.groups) == 0:
        assert not self.scale_by_group, (
            "No groups are defined, i.e. `scale_by_group=[]`"
        )
        if self.method == "standard":
            mean = np.mean(y)
            self.norm_ = mean, np.std(y) / (mean + self.eps)
        else:
            quantiles = np.quantile(y, [0.25, 0.5, 0.75])
            self.norm_ = (
                quantiles[1],
                (quantiles[2] - quantiles[0]) / (quantiles[1] + self.eps),
            )

    elif self.scale_by_group:
        if self.method == "standard":
            self.norm_ = {
                g: X[[g]]
                .assign(y=y)
                .groupby(g, observed=True)
                .agg(mean=("y", "mean"), scale=("y", "std"))
                .assign(scale=lambda x: x.scale / (x["mean"] + self.eps))
                for g in self.groups
            }
        else:
            self.norm_ = {
                g: X[[g]]
                .assign(y=y)
                .groupby(g, observed=True)
                .y.quantile([0.25, 0.5, 0.75])
                .unstack(-1)
                .assign(
                    median=lambda x: x[0.5] + self.eps,
                    scale=lambda x: (x[0.75] - x[0.25] + self.eps)
                    / (x[0.5] + self.eps),
                )[["median", "scale"]]
                for g in self.groups
            }
        # calculate missings
        self.missing_ = {
            group: scales.median().to_dict() for group, scales in self.norm_.items()
        }

    else:
        if self.method == "standard":
            self.norm_ = (
                X[self.groups]
                .assign(y=y)
                .groupby(self.groups, observed=True)
                .agg(mean=("y", "mean"), scale=("y", "std"))
                .assign(scale=lambda x: x.scale / (x["mean"] + self.eps))
            )
        else:
            self.norm_ = (
                X[self.groups]
                .assign(y=y)
                .groupby(self.groups, observed=True)
                .y.quantile([0.25, 0.5, 0.75])
                .unstack(-1)
                .assign(
                    median=lambda x: x[0.5] + self.eps,
                    scale=lambda x: (x[0.75] - x[0.25] + self.eps)
                    / (x[0.5] + self.eps)
                    / 2.0,
                )[["median", "scale"]]
            )
        self.missing_ = self.norm_.median().to_dict()
    return self


# 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 fit(self, y: pd.Series, X: pd.DataFrame):
    """
    Determine scales for each group

    Args:
        y (pd.Series): input data
        X (pd.DataFrame): dataframe with columns for each group defined in ``groups`` parameter.

    Returns:
        self
    """
    if self.coerce_positive is None and not self.log_scale:
        self.coerce_positive = (y >= 0).all()
    y = self._preprocess_y(y)
    if len(self.groups) == 0:
        assert not self.scale_by_group, (
            "No groups are defined, i.e. `scale_by_group=[]`"
        )
        if self.method == "standard":
            self.norm_ = [
                np.mean(y) + self.eps,
                np.std(y) + self.eps,
            ]  # center and scale
        else:
            quantiles = np.quantile(y, [0.25, 0.5, 0.75])
            self.norm_ = [
                quantiles[1] + self.eps,
                (quantiles[2] - quantiles[0]) / 2.0 + self.eps,
            ]  # center and scale
        if not self.center:
            self.norm_[1] = self.norm_[0]
            self.norm_[0] = 0.0

    elif self.scale_by_group:
        if self.method == "standard":
            self.norm_ = {
                g: X[[g]]
                .assign(y=y)
                .groupby(g, observed=True)
                .agg(center=("y", "mean"), scale=("y", "std"))
                .assign(
                    center=lambda x: x["center"] + self.eps,
                    scale=lambda x: x.scale + self.eps,
                )
                for g in self.groups
            }
        else:
            self.norm_ = {
                g: X[[g]]
                .assign(y=y)
                .groupby(g, observed=True)
                .y.quantile([0.25, 0.5, 0.75])
                .unstack(-1)
                .assign(
                    center=lambda x: x[0.5] + self.eps,
                    scale=lambda x: (x[0.75] - x[0.25]) / 2.0 + self.eps,
                )[["center", "scale"]]
                for g in self.groups
            }
        # calculate missings
        if not self.center:  # swap center and scale

            def swap_parameters(norm):
                norm["scale"] = norm["center"]
                norm["center"] = 0.0
                return norm

            self.norm = {g: swap_parameters(norm) for g, norm in self.norm_.items()}
        self.missing_ = {
            group: scales.median().to_dict() for group, scales in self.norm_.items()
        }

    else:
        if self.method == "standard":
            self.norm_ = (
                X[self.groups]
                .assign(y=y)
                .groupby(self.groups, observed=True)
                .agg(center=("y", "mean"), scale=("y", "std"))
                .assign(
                    center=lambda x: x["center"] + self.eps,
                    scale=lambda x: x.scale + self.eps,
                )
            )
        else:
            self.norm_ = (
                X[self.groups]
                .assign(y=y)
                .groupby(self.groups, observed=True)
                .y.quantile([0.25, 0.5, 0.75])
                .unstack(-1)
                .assign(
                    center=lambda x: x[0.5] + self.eps,
                    scale=lambda x: (x[0.75] - x[0.25]) / 2.0 + self.eps,
                )[["center", "scale"]]
            )
        if not self.center:  # swap center and scale
            self.norm_["scale"] = self.norm_["center"]
            self.norm_["center"] = 0.0
        self.missing_ = self.norm_.median().to_dict()
    return self