Ah ok, so I create an instance of the dataset class. And call the labels attribute, which returns a dictionary with id’s and the label. Don’t mind the parameters.
test = CustomDataSet(number_params_baseline=10, los_baseline=0)
label_dict = test.labels
label_dict
>>>{253656: 1, 239289: 1, 282580: 1, 201668: 1, 210325: 1,...} # snippet of the actuall dictionary
len(label_dcit)
>>>11653

To get the original distribution :
classcount = np.zeros(2)
for key in label_dict.keys():
    idx = label_dict[key]
    classcount[idx] += 1
print(classcount)
>>>[ 1048. 10605.]

It seems that the sampler does not change the distribution.
I hope that helps.