how to use deeprenewal programatically

deeprenewal notebook usage

dataset = get_dataset(args.datasource, regenerate=False)
prediction_length = dataset.metadata.prediction_length
freq = dataset.metadata.freq
cardinality = ast.literal_eval(dataset.metadata.feat_static_cat[0].cardinality)
train_ds = dataset.train
test_ds = dataset.test
trainer = Trainer(ctx=mx.context.gpu() if is_gpu&args.use_cuda else mx.context.cpu(), 
                  batch_size=args.batch_size,
                  learning_rate=args.learning_rate,
                  epochs=20,
                  num_batches_per_epoch=args.number_of_batches_per_epoch,
                  clip_gradient=args.clip_gradient,
                  weight_decay=args.weight_decay,
                  hybridize=True) #hybridize false for development
 
estimator = DeepRenewalEstimator(
    prediction_length=prediction_length,
    context_length=prediction_length*args.context_length_multiplier,
    num_layers=args.num_layers,
    num_cells=args.num_cells,
    cell_type=args.cell_type,
    dropout_rate=args.dropout_rate,
    scaling=True,
    lags_seq=np.arange(1,args.num_lags+1).tolist(),
    freq=freq,
    use_feat_dynamic_real=args.use_feat_dynamic_real,
    use_feat_static_cat=args.use_feat_static_cat,
    use_feat_static_real=args.use_feat_static_real,
    cardinality=cardinality if args.use_feat_static_cat else None,
    trainer=trainer,
)
predictor = estimator.train(train_ds, test_ds)
deep_renewal_flat_forecast_it, ts_it = make_evaluation_predictions(
    dataset=test_ds, predictor=predictor, num_samples=100
)
evaluator = IntermittentEvaluator(quantiles=[0.25,0.5,0.75], median=True, calculate_spec=False, round_integer=True)
#DeepAR
agg_metrics, item_metrics = evaluator(
    ts_it, deep_renewal_flat_forecast_it, num_series=len(test_ds)
)