def __init__(self, seed, model_name='DAGMM', tune=False,
                 num_epochs=200, patience=50, lr=1e-4, lr_milestones=[50], batch_size=256,
                 latent_dim=1, n_gmm=4, lambda_energy=0.1, lambda_cov=0.005):
        '''
        The default batch_size is 1024
        The default latent_dim is 1
        The default lambda_cov is 0.005
        '''
        self.utils = Utils()
        self.device = self.utils.get_device()  # get device
        self.seed = seed
        self.tune = tune

        # hyper-parameter
        class Args:
            pass

        self.args = Args()
        self.args.num_epochs = num_epochs
        self.args.patience = patience
        self.args.lr = lr
        self.args.lr_milestones = lr_milestones
        self.args.batch_size = batch_size
        self.args.latent_dim = latent_dim
        self.args.n_gmm = n_gmm
        self.args.lambda_energy = lambda_energy
        self.args.lambda_cov = lambda_cov

        The default batch_size is 1024
        The default latent_dim is 1
        The default lambda_cov is 0.005
        
adbench/baseline/DAGMM/run.py