s390x: fix parsing dimensions for mnist data

This change fixes multiple tests
like MNISTTestCase::test_feature_types
in test/test_datasets.py

Author: AlekseiNikiforovIBM

torchvision/datasets/mnist.py

@@ -525,6 +525,10 @@ def read_sn3_pascalvincent_tensor(path: str, strict: bool = True) -> torch.Tenso
     torch_type = SN3_PASCALVINCENT_TYPEMAP[ty]
     s = [get_int(data[4 * (i + 1) : 4 * (i + 2)]) for i in range(nd)]
 
+    if sys.byteorder == "big":
+        for i in range(len(s)):
+            s[i] = int.from_bytes(s[i].to_bytes(4, byteorder="little"), byteorder="big", signed=False)
+
     parsed = torch.frombuffer(bytearray(data), dtype=torch_type, offset=(4 * (nd + 1)))
 
     # The MNIST format uses the big endian byte order, while `torch.frombuffer` uses whatever the system uses. In case