ValueError: need at least one array to concatenate with sklearn cross_val_predict method

I'm trying to model a binary classification problem with custom cross validation folds with SVM classifier but it gives me the error **need at least one array to concatenate ** with the cross_val_predict. The Code works fine with cv=3 in cros_val_predict but when I use custom_cv, it gives this error.

Below is the code:

from sklearn.model_selection import LeavePOut
import numpy as np
from sklearn.svm import SVC
from time import *
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import cross_val_predict,cross_val_score
clf = SVC(kernel='linear',C=25)
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8],[9,10]])
y = np.array([0,1,1,0,0])
lpo = LeavePOut(2)
print(lpo.get_n_splits(X))
LeavePOut(p=2)
test_index_list=[]
train_index_list=[]
for train_index, test_index in lpo.split(X,y):
  
  if(y[test_index[0]]==y[test_index[1]]):
    pass
  else:
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    train_index_list.append(train_index)
    test_index_list.append(test_index)
custom_cv = zip(train_index_list, test_index_list)
scores = cross_val_score(clf, X, y, cv=custom_cv)

print(scores)
print('accuracy:',scores.mean())
predicted=cross_val_predict(clf,X,y,cv=custom_cv) # error with this line
print('Confusion matrix:',confusion_matrix(labels, predicted))

Below is full trace of error:

ValueError                                Traceback (most recent call last)
<ipython-input-11-d78feac932b2> in <module>()
     31 print(scores)
     32 print('accuracy:',scores.mean())
---> 33 predicted=cross_val_predict(clf,X,y,cv=custom_cv)
     34 
     35 print('Confusion matrix:',confusion_matrix(labels, predicted))

/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py in cross_val_predict(estimator, X, y, groups, cv, n_jobs, verbose, fit_params, pre_dispatch, method)
    758     predictions = [pred_block_i for pred_block_i, _ in prediction_blocks]
    759     test_indices = np.concatenate([indices_i
--> 760                                    for _, indices_i in prediction_blocks])
    761 
    762     if not _check_is_permutation(test_indices, _num_samples(X)):

<__array_function__ internals> in concatenate(*args, **kwargs)

ValueError: need at least one array to concatenate

Any suggestion about how to solve this error?

from ValueError: need at least one array to concatenate with sklearn cross_val_predict method