"""
This file is part of the accompanying code to our manuscript:

<NAME>., <NAME>., <NAME>., and <NAME>.: A note on leveraging synergy in multiple meteorological
datasets with deep learning for rainfall-runoff modeling, Hydrol. Earth Syst. Sci. Discuss.,
https://doi.org/10.5194/hess-2020-221, in review, 2020.

You should have received a copy of the Apache-2.0 license along with the code. If not,
see <https://opensource.org/licenses/Apache-2.0>
"""
import torch.nn as nn

from codebase.modelzoo.cudalstm import CudaLSTM


def get_model(cfg: dict) -> nn.Module:

    if cfg["model"] == "cudalstm":
        model = CudaLSTM(cfg=cfg)
    else:
        raise NotImplementedError(cfg["model"])

    return model

"""
This file is part of the accompanying code to our manuscript:

<NAME>., <NAME>., <NAME>., and <NAME>.: A note on leveraging synergy in multiple meteorological
datasets with deep learning for rainfall-runoff modeling, Hydrol. Earth Syst. Sci. Discuss.,
https://doi.org/10.5194/hess-2020-221, in review, 2020.

You should have received a copy of the Apache-2.0 license along with the code. If not,
see <https://opensource.org/licenses/Apache-2.0>
"""
import torch.nn as nn

from codebase.modelzoo.cudalstm import CudaLSTM


def get_model(cfg: dict) -> nn.Module:

    if cfg["model"] == "cudalstm":
        model = CudaLSTM(cfg=cfg)
    else:
        raise NotImplementedError(cfg["model"])

    return model

This file is part of the accompanying code to our manuscript:  <NAME>., <NAME>., <NAME>., and <NAME>.: A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-221, in review, 2020.  You should have received a copy of the Apache-2.0 license along with the code. If not, see <https://opensource.org/licenses/Apache-2.0>