from pytorch_lightning import LightningModule

from models.simple_cnn_model import SimpleCNNModel


def get_model(
    args,
    device: str,
    hparams: dict,
    trial=None,
) -> LightningModule:
    model_type = args.TRAIN.MODEL_TYPE.lower()

    if model_type == 'simple_cnn':
        model = SimpleCNNModel(
            args=args,
            device=device,
            hparams=hparams,
            in_channel=args.DATA.INPUT_DIM,
            out_channel=len(args.DATA.CLASSES),
            trial=trial,
        )
    else:
        raise NotImplementedError()

    return model

cef34bd0651be5db9a79c1f5f1fa334c8b241c12
<mask token>

<mask token>


def get_model(args, device: str, hparams: dict, trial=None) ->LightningModule:
    model_type = args.TRAIN.MODEL_TYPE.lower()
    if model_type == 'simple_cnn':
        model = SimpleCNNModel(args=args, device=device, hparams=hparams,
            in_channel=args.DATA.INPUT_DIM, out_channel=len(args.DATA.
            CLASSES), trial=trial)
    else:
        raise NotImplementedError()
    return model

from pytorch_lightning import LightningModule
from models.simple_cnn_model import SimpleCNNModel


def get_model(args, device: str, hparams: dict, trial=None) ->LightningModule:
    model_type = args.TRAIN.MODEL_TYPE.lower()
    if model_type == 'simple_cnn':
        model = SimpleCNNModel(args=args, device=device, hparams=hparams,
            in_channel=args.DATA.INPUT_DIM, out_channel=len(args.DATA.
            CLASSES), trial=trial)
    else:
        raise NotImplementedError()
    return model

from pytorch_lightning import LightningModule

from models.simple_cnn_model import SimpleCNNModel


def get_model(
    args,
    device: str,
    hparams: dict,
    trial=None,
) -> LightningModule:
    model_type = args.TRAIN.MODEL_TYPE.lower()

    if model_type == 'simple_cnn':
        model = SimpleCNNModel(
            args=args,
            device=device,
            hparams=hparams,
            in_channel=args.DATA.INPUT_DIM,
            out_channel=len(args.DATA.CLASSES),
            trial=trial,
        )
    else:
        raise NotImplementedError()

    return model
