[Python] SHAP - instances that have more than one dimension

I am very new to SHAP and I would like to give it a try, but I am having some difficulty.

The model is already trained and seems to perform well. I then use the training data to test SHAP with. It looks like so:

var_Braeburn var_Cripps Pink var_Dazzle var_Fuji var_Granny Smith \
0 1 0 0 0 0
1 0 1 0 0 0
2 0 1 0 0 0
3 0 1 0 0 0
4 0 1 0 0 0

var_Other Variety var_Royal Gala (Tenroy) root_CG202 root_M793 \
0 0 0 0 0
1 0 0 1 0
2 0 0 1 0
3 0 0 0 0
4 0 0 0 0

root_MM106 ... frt_BioRich Organic Compost_single \
0 1 ... 0
1 0 ... 0
2 0 ... 0
3 1 ... 0
4 1 ... 0

frt_Biomin Boron_single frt_Biomin Zinc_single \
0 0 1
1 0 0
2 0 0
3 0 0
4 0 0

frt_Fertco Brimstone90 sulphur_single frt_Fertco Guano _single \
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0

frt_Gro Mn_multiple frt_Gro Mn_single frt_Organic Mag Super_multiple \
0 0 0 0
1 1 0 1
2 1 0 1
3 1 0 1
4 1 0 1

frt_Organic Mag Super_single frt_Other Fertiliser
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0


I then do explainer = shap.Explainer(model) and shap_values = explainer(X_train)

This runs without error and shap_values gives me this:

.values =
array([[[ 0.00775555, -0.00775555],
[-0.03221035, 0.03221035],
[-0.0027203 , 0.0027203 ],
...,
[ 0.00259787, -0.00259787],
[-0.00459262, 0.00459262],
[-0.0303394 , 0.0303394 ]],

[[-0.00068313, 0.00068313],
[-0.03006355, 0.03006355],
[-0.00245706, 0.00245706],
...,
[-0.00418809, 0.00418809],
[-0.00088372, 0.00088372],
[-0.00030019, 0.00030019]],

[[-0.00068313, 0.00068313],
[-0.03006355, 0.03006355],
[-0.00245706, 0.00245706],
...,
[-0.00418809, 0.00418809],
[-0.00088372, 0.00088372],
[-0.00030019, 0.00030019]],

...,


However, when I then run shap.plots.beeswarm(shap_values), I get the following error:

ValueError: The beeswarm plot does not support plotting explanations with instances that have more than one dimension!

What am I doing wrong here?

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