RuntimeError: expected scalar type Float but found Double

Question

#pyhon
機械学習のN-beatsをpythonのpycharm)実行しようとして、以下のエラーが出てしまいました。
Traceback (most recent call last):
File "C:/Users/takaa/PycharmProjects/pythonProject4/N-beats3.py", line 509, in <module>
verbose=True)
File "C:\Users\takaa\PycharmProjects\pythonProject6\venv\lib\site-packages\darts\utils\torch.py", line 112, in decorator
return decorated(self, *args, **kwargs)
RuntimeError: expected scalar type Float but found Double
Process finished with exit code 1

以下のリンクを参考にしています.

[https://towardsdatascience.com/n-beats-unleashed-deep-forecasting-using-neural-basis-expansion-analysis-in-python-343dd6307010
]

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import sys
import missingno as mno

import warnings
warnings.filterwarnings("ignore")
import logging
logging.disable(logging.CRITICAL)


from darts import TimeSeries, concatenate
from darts.dataprocessing.transformers import Scaler
from darts.models import NBEATSModel
from darts.metrics import mape, rmse
from darts.utils.timeseries_generation import datetime_attribute_timeseries
from darts.utils.likelihood_models import QuantileRegression

#中略
model = NBEATSModel(    input_chunk_length=INLEN,
                        output_chunk_length=N_FC,
                        num_stacks=BLOCKS,
                        layer_widths=LWIDTH,
                        batch_size=BATCH,
                        n_epochs=EPOCHS,
                        nr_epochs_val_period=VALWAIT,
                        likelihood=QuantileRegression(QUANTILES),
                        optimizer_kwargs={"lr": LEARN},
                        model_name="NBEATS_EnergyES",
                        log_tensorboard=True,
                        generic_architecture=True,
                        random_state=RAND,
                        force_reset=True,
                        save_checkpoints=True
                    )

# training: load a saved model or (re)train
print(model)
print(type(model))
#model=model.double()
if LOAD:
    print("have loaded a previously saved model from disk:" + mpath)
    model = NBEATSModel.load_model(mpath)                            # load previously model from disk
else:
    model.fit(  series=ts_ttrain,
                past_covariates=cov_t,
                val_series=ts_ttest,
                val_past_covariates=cov_t,
                verbose=True)
    print("have saved the model after training:", mpath)
    model.save_model(mpath)