FCM lags suspicious results

Hi there,
I wanted to see the effects of lagged variables on the car dataset so I did a quick sanity check:

from causallearn.search.FCMBased import lingam

for lags in range(1,4):
  print("\n\nLags %d" % lags)
  print("Total columns %d " % data.shape[1])
  model = lingam.VARLiNGAM(lags=lags,random_state=1,prune=False)
  model.fit(data)

  print("Causal Order features %d " % len(model.causal_order_))
  print(model.causal_order_)
  print("Matrix lags %d " % model.adjacency_matrices_.shape[0])
  #print(model.adjacency_matrices_[0])
  #print(model.adjacency_matrices_[1])
  #print(model.residuals_)

The output doesn't show any influence of the lag factor, see below:

Lags 1
Total columns 10 
Causal Order features 10 
[4, 8, 1, 6, 3, 0, 2, 7, 5, 9]
Matrix lags 2 


Lags 2
Total columns 10 
Causal Order features 10 
[4, 8, 1, 6, 3, 0, 2, 7, 5, 9]
Matrix lags 2 


Lags 3
Total columns 10 
Causal Order features 10 
[4, 8, 1, 6, 3, 0, 2, 7, 5, 9]
Matrix lags 2 

I would expect that the matrix lag and the causal order features increases as the lag increases?
For example when Lag is equal to 2, I should see double the columns and features?

Start with causallearn.search.FCMBased.lingam.VARLiNGAM and reproduce the provided car-dataset snippet for lags 1 through 3. Inspect how causal_order_ and adjacency_matrices_ are defined and populated, then resolve whether the observed shapes are intended or indicate a lag-handling bug.