diff --git a/monai/engines/evaluator.py b/monai/engines/evaluator.py
index 43964ee8bc..7c6ddd5bdd 100644
--- a/monai/engines/evaluator.py
+++ b/monai/engines/evaluator.py
@@ -295,7 +295,6 @@ def _iteration(self, engine: SupervisedEvaluator, batchdata: dict[str, torch.Ten
 
         # put iteration outputs into engine.state
         engine.state.output = {Keys.IMAGE: inputs, Keys.LABEL: targets}
-
         # execute forward computation
         with engine.mode(engine.network):
             if engine.amp:
diff --git a/monai/networks/utils.py b/monai/networks/utils.py
index d5c0629c05..b79ae8e9bd 100644
--- a/monai/networks/utils.py
+++ b/monai/networks/utils.py
@@ -375,17 +375,19 @@ def eval_mode(*nets: nn.Module):
             print(p(t).sum().backward())  # will correctly raise an exception as gradients are calculated
     """
 
-    # Get original state of network(s)
-    training = [n for n in nets if n.training]
+    # Get original state of network(s).
+    # Check the training attribute in case it's TensorRT based models which don't have this attribute.
+    training = [n for n in nets if hasattr(n, "training") and n.training]
 
     try:
         # set to eval mode
         with torch.no_grad():
-            yield [n.eval() for n in nets]
+            yield [n.eval() if hasattr(n, "eval") else n for n in nets]
     finally:
         # Return required networks to training
         for n in training:
-            n.train()
+            if hasattr(n, "train"):
+                n.train()
 
 
 @contextmanager
@@ -410,16 +412,18 @@ def train_mode(*nets: nn.Module):
     """
 
     # Get original state of network(s)
-    eval_list = [n for n in nets if not n.training]
+    # Check the training attribute in case it's TensorRT based models which don't have this attribute.
+    eval_list = [n for n in nets if hasattr(n, "training") and (not n.training)]
 
     try:
         # set to train mode
         with torch.set_grad_enabled(True):
-            yield [n.train() for n in nets]
+            yield [n.train() if hasattr(n, "train") else n for n in nets]
     finally:
         # Return required networks to eval_list
         for n in eval_list:
-            n.eval()
+            if hasattr(n, "eval"):
+                n.eval()
 
 
 def get_state_dict(obj: torch.nn.Module | Mapping):