GluonTSDataError: Input for field "target" does not have the requireddimension (field: target, ndim observed: 2, expected ndim: 1) about gluonts HOT 5 OPEN

```
estimator = GPVAREstimator(
    prediction_length=prediction_length, 
    target_dim=len(vital_signs),
    freq='1H',
    trainer=Trainer(epochs=5, num_batches_per_epoch=50),
)
predictor = estimator.train(training_data=train_ds)
```

**Exception: Reached maximum number of idle transformation calls.
This means the transformation looped over 423 inputs without returning any output.
This occurred in the following transformation:
gluonts.transform.split.InstanceSplitter(dummy_value=0.0, forecast_start_field='forecast_start', future_length=1, instance_sampler=ExpectedNumInstanceSampler(axis=-1, min_past=173, min_future=1, num_instances=1.0, total_length=0, n=0), is_pad_field='is_pad', lead_time=0, output_NTC=True, past_length=173, start_field='start', target_field='target', time_series_fields=['time_feat', 'observed_values'])**

```
[109, 15, 9, 15, 16, 11, 1, 15, 5, 7, 17, 5, 4, 40, 7, 7, 3, 13, 11, 7, 5, 8, 18, 10, 12, 25, 5, 5, 5, 3, 3, 9, 9, 20, 2, 3, 15, 2, 10, 24, 7, 17, 8, 16, 4, 10, 11, 10, 9, 15, 9, 7, 14, 10, 3, 3, 20, 11, 7, 9, 18, 8, 11, 13, 9, 3, 7, 11, 62, 5, 8, 23, 21, 5, 15, 16, 14, 11, 5, 6, 7, 5, 12, 2, 12, 3, 6, 5, 12, 16, 15, 6, 5, 12, 17, 20, 24, 7, 29, 18, 9, 5, 9, 11, 13, 6, 21, 6, 8, 24, 3, 3, 12, 5, 4, 4, 1, 1, 32, 31, 5, 3, 9, 28, 52, 13, 9, 18, 13, 1, 6, 6, 44, 9, 9, 9, 5, 7, 9, 10, 6, 15, 13, 8, 6, 11, 4, 13, 3, 21, 12, 18, 23, 17, 8, 14, 6, 18, 20, 13, 9, 35, 9, 7, 8, 4, 11, 15, 4, 7, 9, 10, 13, 9, 17, 25, 30, 9, 9, 5, 8, 13, 5, 5, 18, 9, 12, 9, 45, 10, 18, 9, 35, 4, 9, 4, 4, 3, 13, 11, 2, 4, 5, 8, 7, 14, 19, 38, 3, 2, 11, 4, 6, 10, 6, 25, 7, 3, 6, 9, 7, 9, 15, 14, 7, 3, 3, 1, 11, 9, 8, 6, 11, 10, 13, 6, 23, 42, 3, 9, 5, 5, 11, 5, 13, 4, 10, 4, 8, 10, 1, 11, 15, 3, 9, 12, 6, 12, 31, 6, 3, 2, 4, 4, 21, 9, 9, 10, 8, 5, 11, 8, 20, 2, 5, 8, 6, 12, 11, 13, 10, 9, 13, 5, 7, 4, 15, 3, 23, 15, 4, 8, 3, 7, 9, 41, 11, 9, 11, 10, 7, 3, 4, 4, 1, 7, 10, 3, 5, 2, 21, 10, 7, 12, 6, 6, 8, 5, 9, 12, 12, 4, 31, 8, 8, 9, 4, 8, 6, 8, 6, 3, 4, 20, 13, 5, 4, 8, 18, 19, 11, 8, 13, 6, 4, 5, 6, 11, 2, 9, 20, 11, 11, 5, 2, 1, 23, 10, 7, 11, 12, 4, 11, 21, 2, 13, 11, 9, 13, 6, 8, 4, 6, 29, 11, 13, 10, 9, 12, 1, 11, 10, 6, 9, 19, 8, 4, 5, 13, 2, 8, 3, 13, 11, 8, 25, 11, 4, 7, 32, 3, 11, 14, 13, 19, 7, 16, 6, 7, 15, 5, 8, 3, 21, 17, 10, 18, 3, 10, 12, 10, 7, 10, 3, 3, 3, 20, 6, 10, 13, 4, 3, 19, 2, 21, 12, 5, 4, 4, 6, 15, 1, 13, 3, 5, 5, 5, 9, 32, 15, 5, 2, 7, 14, 4, 6, 9, 9, 10, 12, 9, 12, 13, 5, 8, 6, 5, 9, 1, 14, 22, 12, 11, 1, 10, 14, 3, 17, 3, 5, 15, 9, 3, 8, 3, 3, 4, 7, 6, 2, 3, 3, 14, 7, 3, 19, 5, 9, 1, 9, 6, 10, 3, 7, 12, 18, 15, 5, 10, 10, 6, 12, 18, 1, 5, 4, 8, 11, 1, 3, 3, 3, 6, 8, 11, 9, 4, 13, 11, 2, 2, 12, 13, 5, 5, 5, 9, 11, 1, 7, 9, 9, 5, 5, 3, 15, 5, 5, 15, 1, 5, 10, 4, 12, 17, 5, 8, 3, 3, 11, 3, 13, 13, 12, 3, 38, 5, 5, 15, 5, 6, 4, 4, 4, 15, 7, 11, 8, 3, 4, 6, 15, 11, 6, 5, 24, 12, 11, 6, 7, 2, 7, 4, 9, 34, 8, 11, 4, 26, 12, 24, 3, 4, 6, 2, 8, 1, 4, 7, 7, 9, 6, 10, 12, 8, 3, 21, 3, 11, 4, 4, 5, 12, 17, 5, 5, 16, 17, 11, 20, 3, 5, 7, 43, 20, 31, 4, 13, 9, 17, 3, 7, 2, 11, 15, 14, 5, 12, 16, 9, 7, 8, 10, 23, 13, 6, 6, 9, 5, 4, 6, 9, 39, 26, 6, 3, 6, 7, 8, 4, 5, 10, 1, 8, 3, 14, 8, 16, 3, 6, 10, 3, 5, 13, 3, 37, 7, 9, 8, 8, 8, 4, 6, 9, 13, 6, 15, 20, 8, 23, 11, 37, 5, 4, 4, 1, 15, 10, 13, 4, 2, 3, 5, 11, 7, 8, 7, 4, 5, 4, 5, 5, 8, 6, 2, 1, 13, 1, 11, 7, 9, 10, 6, 7, 10, 146, 8, 7, 10, 8, 21, 8, 12, 3, 15, 12, 14, 2, 3, 3, 43, 20, 5, 11, 8, 5, 2, 11, 22, 10, 4, 11, 9, 11, 13, 3, 45, 13, 5, 7, 11, 5, 10, 3]

```

```
estimator = DeepVAREstimator(
    prediction_length=prediction_length,
    context_length=context_length,
    freq="1H",
    num_layers = 2,
    distr_output=MultivariateGaussianOutput(dim=target.shape[1]),
    scaling=False,
    rank=7,
    batch_size=32,
    target_dim=7,
    trainer=Trainer(epochs = 30, ctx="cpu", learning_rate=1e-3, num_batches_per_epoch=100)
)
predictor = estimator.train(
    training_data=train_ds,
    shuffle_buffer_length=1024,
    cache_data=False,
)
```

```
Exception: Reached maximum number of idle transformation calls.
This means the transformation looped over 619 inputs without returning any output.
This occurred in the following transformation:
gluonts.transform.split.InstanceSplitter(dummy_value=0.0, forecast_start_field='forecast_start', future_length=1, instance_sampler=ExpectedNumInstanceSampler(axis=-1, min_past=173, min_future=1, num_instances=1.0, total_length=0, n=0), is_pad_field='is_pad', lead_time=0, output_NTC=True, past_length=173, start_field='start', target_field='target', time_series_fields=['time_feat', 'observed_values'])