diff --git a/questions/18_implement-k-fold-cross-validation/pytorch/starter_code.py b/questions/18_implement-k-fold-cross-validation/pytorch/starter_code.py
index c881f619..6b0558c3 100644
--- a/questions/18_implement-k-fold-cross-validation/pytorch/starter_code.py
+++ b/questions/18_implement-k-fold-cross-validation/pytorch/starter_code.py
@@ -1,6 +1,9 @@
 import torch
 
-def k_fold_cross_validation(X, y, k=5, shuffle=True) -> list[tuple[list[int], list[int]]]:
+
+def k_fold_cross_validation(
+    X, y, k=5, shuffle=True
+) -> list[tuple[list[int], list[int]]]:
     """
     Return train/test index splits for k-fold cross-validation using PyTorch.
     X: Tensor or convertible of shape (n_samples, ...)
@@ -9,25 +12,4 @@ def k_fold_cross_validation(X, y, k=5, shuffle=True) -> list[tuple[list[int], li
     shuffle: whether to shuffle indices before splitting
     Returns list of (train_idx, test_idx) pairs, each as Python lists of ints.
     """
-    X_t = torch.as_tensor(X)
-    n_samples = X_t.size(0)
-    indices = torch.arange(n_samples)
-    if shuffle:
-        indices = indices[torch.randperm(n_samples)]
-    # compute fold sizes
-    base = n_samples // k
-    extras = n_samples % k
-    fold_sizes = [base + (1 if i < extras else 0) for i in range(k)]
-    # split into folds
-    folds = []
-    start = 0
-    for fs in fold_sizes:
-        folds.append(indices[start:start+fs].tolist())
-        start += fs
-    # build train/test pairs
-    result = []
-    for i in range(k):
-        test_idx = folds[i]
-        train_idx = [idx for j, f in enumerate(folds) if j != i for idx in f]
-        result.append((train_idx, test_idx))
-    return result
+    # Your code here