Prediction of first point depends on pred_length

[satyrmipt]

I predict for 1, 2, and 3 steps forward in time with the same lookback_len. Why predictions for first steps are different in all three predictions? It is the same point in time...
Do first prediction depends on consecutive ones like first token depends on consecutive ones in NLP due to approaches like beam search?

from iTransformer import iTransformer
import torch

m1=iTransformer(
    num_variates=1,
    lookback_len=7, 
    depth=1,
    dim=1,
    pred_length=(1,2,3)                     
)

time_series = torch.ones(1, 7, 1)  # (batch, lookback len, variates)
preds = m1(time_series) # Dict[int, Tensor[batch, pred_length, variate]]
# print(np.array(time_series))
for i in preds.keys():
  print(f"Length={i} result:\n", preds[i].detach().numpy() , preds[i].detach().numpy().shape, '\n')

Length=1 result:
[[[-0.13998163]]] (1, 1, 1)

Length=2 result:
[[[-0.03886282]
[ 0.41442168]]] (1, 2, 1)

Length=3 result:
[[[ 0.39561594]
[-0.61296153]
[ 0.08078277]]] (1, 3, 1)

[lucidrains]

@satyrmipt yes indeed, i think this is what they did in the paper (please correct me if i'm wrong)

it should just be one linear projection to the maximum predicted length