Multivariate-Flow-Solar：an error is reported when flow_type='MAF'

[c247274901]

Thank you for visiting my issue!
I'm using my own dataset，dim=1：

estimator = TempFlowEstimator(
    freq=custom_ds_metadata["freq"], 
    prediction_length=custom_ds_metadata["prediction_length"],
    target_dim=dim,
    cell_type='GRU',
    input_size=6,
    scaling=True,
    dequantize=True,
    flow_type='MAF',
    trainer=Trainer(device=device,
                    epochs=20,
                    learning_rate=1e-4,
                    num_batches_per_epoch=20,
                    batch_size=256)
)

train_output = estimator.train_model(training)
predictor = train_output.predictor

Error message or code output

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_12548\2258705580.py in <module>
     16 )
     17 
---> 18 train_output = estimator.train_model(training)
     19 predictor = train_output.predictor

~\Downloads\pytorch-ts-master\pytorch-ts-master\pts\model\estimator.py in train_model(self, training_data, validation_data, num_workers, prefetch_factor, shuffle_buffer_length, cache_data, **kwargs)
    101         transformation = self.create_transformation()
    102 
--> 103         trained_net = self.create_training_network(self.trainer.device)
    104 
    105         input_names = get_module_forward_input_names(trained_net)

~\Downloads\pytorch-ts-master\pytorch-ts-master\pts\model\tempflow\tempflow_estimator.py in create_training_network(self, device)
    206             n_hidden=self.n_hidden,
    207             conditioning_length=self.conditioning_length,
--> 208             dequantize=self.dequantize,
    209         ).to(device)
    210 

c:\users\pc\appdata\local\programs\python\python37\lib\site-packages\gluonts\core\component.py in init_wrapper(*args, **kwargs)
    342                 self.__class__.__repr__ = validated_repr
    343 
--> 344             return init(self, **all_args)
    345 
    346         # attach the Pydantic model as the attribute of the initializer wrapper

~\Downloads\pytorch-ts-master\pytorch-ts-master\pts\model\tempflow\tempflow_network.py in __init__(self, input_size, num_layers, num_cells, cell_type, history_length, context_length, prediction_length, dropout_rate, lags_seq, target_dim, conditioning_length, flow_type, n_blocks, hidden_size, n_hidden, dequantize, cardinality, embedding_dimension, scaling, **kwargs)
     65             n_hidden=n_hidden,
     66             hidden_size=hidden_size,
---> 67             cond_label_size=conditioning_length,
     68         )
     69         self.dequantize = dequantize

~\Downloads\pytorch-ts-master\pytorch-ts-master\pts\modules\flows.py in __init__(self, n_blocks, input_size, hidden_size, n_hidden, cond_label_size, activation, input_order, batch_norm)
    405                     activation,
    406                     input_order,
--> 407                     self.input_degrees,
    408                 )
    409             ]

~\Downloads\pytorch-ts-master\pytorch-ts-master\pts\modules\flows.py in __init__(self, input_size, hidden_size, n_hidden, cond_label_size, activation, input_order, input_degrees)
    250         # create masks
    251         masks, self.input_degrees = create_masks(
--> 252             input_size, hidden_size, n_hidden, input_order, input_degrees
    253         )
    254 

~\Downloads\pytorch-ts-master\pytorch-ts-master\pts\modules\flows.py in create_masks(input_size, hidden_size, n_hidden, input_order, input_degrees)
     22         )
     23         for _ in range(n_hidden + 1):
---> 24             degrees += [torch.arange(hidden_size) % (input_size - 1)]
     25         degrees += (
     26             [torch.arange(input_size) % input_size - 1]

RuntimeError: ZeroDivisionError

Environment

Python version: 3.7.0
GluonTS version: 0.9.0
MXNet version: without MXNet

[c247274901]

when flow_type=‘RealNVP’,there is no problem.