Error in TimeSeies Dataset PyTorch forecasting

PyTorch TimeSeries Dataset compaints about NaN; when there is no NaN in the data.

To confirm, created a sample dataset with no NaN values in the data of 1000 rows.

    df[lambda x: x.index <= training_cutoff],
    time_idx='index',
    target='Target',
    group_ids=['Region'],
    min_encoder_length=1,
    max_encoder_length=max_encoder_length,
    min_prediction_length=1,
    max_prediction_length=max_prediction_length,
    static_categoricals=['categorical_feature'],
    time_varying_unknown_reals=['numerical_feature1'],
    target_normalizer=GroupNormalizer(groups=['Target']),
    add_relative_time_idx=True,
    add_target_scales=True,
    add_encoder_length=True)

It raises following error:

ValueError: 995 (100.00%) of Target_scale values were found to be NA or infinite (even after encoding). NA values are not allowed `allow_missing_timesteps` refers to missing rows, not to missing values. Possible strategies to fix the issue are (a) dropping the variable Target_scale, (b) using `NaNLabelEncoder(add_nan=True)` for categorical variables, (c) filling missing values and/or (d) optionally adding a variable indicating filled values

What is causing this issue and what is the possible fix?