diff --git a/tutorials/machine_learning/TMVA_SOFIE_GNN.py b/tutorials/machine_learning/TMVA_SOFIE_GNN.py
index 4112effc6fa51..121fb14d86ebd 100644
--- a/tutorials/machine_learning/TMVA_SOFIE_GNN.py
+++ b/tutorials/machine_learning/TMVA_SOFIE_GNN.py
@@ -13,6 +13,7 @@
 ##
 ## \author
 
+import cppyy
 import time
 
 import numpy as np
@@ -200,6 +201,9 @@ def c(x):
 
         receivers = np.ascontiguousarray(graphData["receivers"], dtype=np.int64)
         senders = np.ascontiguousarray(graphData["senders"], dtype=np.int64)
+        i64p = cppyy.ll.cast["const int64_t*"]
+        receivers_ptr = i64p(receivers.ctypes.data)
+        senders_ptr = i64p(senders.ctypes.data)
 
         latent = self._as_arrays(
             self.encoder_session.infer(c(graphData["nodes"]), c(graphData["edges"]), c(graphData["globals"])),
@@ -210,8 +214,8 @@ def c(x):
         for _ in range(processing_steps):
             core_input = tuple(np.concatenate([a, b], axis=1) for a, b in zip(latent0, latent))
             latent = self._as_arrays(
-                self.core_session.infer(c(core_input[0]), c(core_input[1]), c(core_input[2]), receivers, senders),
-                n_nodes, n_edges,
+            self.core_session.infer(c(core_input[0]), c(core_input[1]), c(core_input[2]), receivers_ptr, senders_ptr),
+            n_nodes, n_edges,
             )
             decoded = self._as_arrays(
                 self.decoder_session.infer(c(latent[0]), c(latent[1]), c(latent[2])), n_nodes, n_edges