Fix TorchScript compatibility and invalid test input shape for DiNTS

Signed-off-by: Adrian Caderno <adriancaderno@gmail.com>

Author: Cado87

monai/networks/nets/dints.py

@@ -490,6 +490,7 @@ def __init__(
     def weight_parameters(self):
         return [param for name, param in self.named_parameters()]
 
+    @torch.jit.unused
     def _check_input_size(self, spatial_shape):
         """
         Validate that input spatial dimensions satisfy the divisibility requirement.
@@ -521,7 +522,8 @@ def forward(self, x: torch.Tensor):
             ValueError: if any spatial dimension of ``x`` is not divisible by
                 ``2 ** (num_depths + int(use_downsample))``.
         """
-        self._check_input_size(x.shape[2:])
+        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
+            self._check_input_size(x.shape[2:])
         inputs = []
         for d in range(self.num_depths):
             # allow multi-resolution input

tests/networks/nets/test_dints_network.py

@@ -84,8 +84,8 @@
             "use_downsample": True,
             "spatial_dims": 2,
         },
-        (2, 2, 32, 16),
-        (2, 2, 32, 16),
+        (2, 2, 32, 32),  # use_downsample=True, num_depths=4 -> factor=32; both dims must be divisible by 32
+        (2, 2, 32, 32),
     ]
 ]
 if torch.cuda.is_available():