How to generate arbitrary high dimensional connectivity structures for scipy.ndimage.label

I have some high dimensional boolean data, in this example an array with 4 dimensions, but this is arbitrary:

X.shape
 (3, 2, 66, 241)

I want to group the dataset into connected regions of True values, which can be done with scipy.ndimage.label, with the aid of a connectivity structure which says which points in the array should be considered to touch. The default 2-D structure is a cross:

[[0,1,0],
 [1,1,1],
 [0,1,0]]

Which can be easily extended to high dimensions if all those dimensions are connected. However I want to programmatically generate such a structure where I have a list of which dims are connected to which:

#We want to find connections across dims 2 and 3 across each slice of dims 0 and 1:
dim_connections=[[0],[1],[2,3]]

#Now we want two separate connected subspaces in our data:
dim_connections=[[0,1],[2,3]]

For individual cases I can work out with hard-thinking how to generate the correct structure tensor, but I am struggling to work out the general rule! For clarity I want something like:

mystructure=construct_arbitrary_structure(ndim, dim_connections)
the_correct_result=scipy.ndimage.label(X,structure=my_structure)

from How to generate arbitrary high dimensional connectivity structures for scipy.ndimage.label