diff --git a/monai/networks/nets/dints.py b/monai/networks/nets/dints.py
index eea9f351d8..437789ef0c 100644
--- a/monai/networks/nets/dints.py
+++ b/monai/networks/nets/dints.py
@@ -40,7 +40,7 @@
 class CellInterface(torch.nn.Module):
     """interface for torchscriptable Cell"""
 
-    def forward(self, x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:  # type: ignore
+    def forward(self, x: torch.Tensor, weight) -> torch.Tensor:  # type: ignore
         pass
 
 
@@ -170,7 +170,7 @@ def __init__(self, c: int, ops: dict, arch_code_c=None):
             if arch_c > 0:
                 self.ops.append(ops[op_name](c))
 
-    def forward(self, x: torch.Tensor, weight: torch.Tensor):
+    def forward(self, x: torch.Tensor, weight: torch.Tensor | None = None):
         """
         Args:
             x: input tensor.
@@ -179,9 +179,10 @@ def forward(self, x: torch.Tensor, weight: torch.Tensor):
             out: weighted average of the operation results.
         """
         out = 0.0
-        weight = weight.to(x)
+        if weight is not None:
+            weight = weight.to(x)
         for idx, _op in enumerate(self.ops):
-            out = out + _op(x) * weight[idx]
+            out = (out + _op(x)) if weight is None else out + _op(x) * weight[idx]
         return out
 
 
@@ -297,7 +298,7 @@ def __init__(
 
         self.op = MixedOp(c, self.OPS, arch_code_c)
 
-    def forward(self, x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
+    def forward(self, x: torch.Tensor, weight: torch.Tensor | None) -> torch.Tensor:
         """
         Args:
             x: input tensor
@@ -669,15 +670,13 @@ def forward(self, x: list[torch.Tensor]) -> list[torch.Tensor]:
             x: input tensor.
         """
         # generate path activation probability
-        inputs, outputs = x, [torch.tensor(0.0).to(x[0])] * self.num_depths
+        inputs = x
         for blk_idx in range(self.num_blocks):
-            outputs = [torch.tensor(0.0).to(x[0])] * self.num_depths
+            outputs = [torch.tensor(0.0, dtype=x[0].dtype, device=x[0].device)] * self.num_depths
             for res_idx, activation in enumerate(self.arch_code_a[blk_idx].data):
                 if activation:
                     mod: CellInterface = self.cell_tree[str((blk_idx, res_idx))]
-                    _out = mod.forward(
-                        x=inputs[self.arch_code2in[res_idx]], weight=torch.ones_like(self.arch_code_c[blk_idx, res_idx])
-                    )
+                    _out = mod.forward(x=inputs[self.arch_code2in[res_idx]], weight=None)
                     outputs[self.arch_code2out[res_idx]] = outputs[self.arch_code2out[res_idx]] + _out
             inputs = outputs
 
@@ -885,13 +884,13 @@ def get_ram_cost_usage(self, in_size, full: bool = False):
         sizes = []
         for res_idx in range(self.num_depths):
             sizes.append(batch_size * self.filter_nums[res_idx] * (image_size // (2**res_idx)).prod())
-        sizes = torch.tensor(sizes).to(torch.float32).to(self.device) / (2 ** (int(self.use_downsample)))
+        sizes = torch.tensor(sizes, dtype=torch.float32, device=self.device) / (2 ** (int(self.use_downsample)))
         probs_a, arch_code_prob_a = self.get_prob_a(child=False)
         cell_prob = F.softmax(self.log_alpha_c, dim=-1)
         if full:
             arch_code_prob_a = arch_code_prob_a.detach()
             arch_code_prob_a.fill_(1)
-        ram_cost = torch.from_numpy(self.ram_cost).to(torch.float32).to(self.device)
+        ram_cost = torch.from_numpy(self.ram_cost).to(dtype=torch.float32, device=self.device)
         usage = 0.0
         for blk_idx in range(self.num_blocks):
             # node activation for input