Describe the bug
Hi, I'm using a deterministic TFT model for forecast. But .predict always returns a different output.
Here's the model detail.

TFTModel(hidden_size=256, lstm_layers=4, num_attention_heads=32, full_attention=False, feed_forward=GatedResidualNetwork, dropout=0.23125297663928174, hidden_continuous_size=8, categorical_embedding_sizes=None, add_relative_index=False, loss_fn=None, likelihood=None, norm_type=LayerNorm, use_static_covariates=True, batch_size=32, n_epochs=20, nr_epochs_val_period=1, add_encoders={'cyclic': {'future': ['dayofweek', 'month', 'quarter', 'dayofyear', 'weekofyear']}, 'datetime_attribute': {'future': ['is_month_end', 'is_month_start', 'is_year_start', 'is_year_end']}, 'position': {'past': ['relative'], 'future': ['relative']}, 'transformer': Scaler}, pl_trainer_kwargs={'accelerator': 'gpu', 'devices': [0], 'callbacks': [<pytorch_lightning.callbacks.early_stopping.EarlyStopping object at 0x000001EC20E37100>]}, model_name=tft_model, force_reset=True, save_checkpoints=True, input_chunk_length=31, output_chunk_length=21, optimizer_kwargs={'lr': 0.0001884674313235514})

To Reproduce

model = TFTModel.load("./tft_model_deterministic.pt", map_location=torch.device('cpu'))
model.to_cpu()

preds = model.predict(
    series=[data.split_after(pd.Timestamp("2023-11-14"))[0] for data in target_scaled],
    n=model.output_chunk_length,
    future_covariates=[future_cov_scaled for _ in range(len(train_target_scaled))],
    past_covariates=[past_cov_scaled for _ in range(len(train_target_scaled))],
)

Expected behavior
The .predict should have returned the same result every time it's called, but it's not.

System (please complete the following information):

• Python version: [e.g. 3.9.13]
• darts version [e.g. 0.27.0]

Additional context
Add any other context about the problem here.