fix: return mu (not mu+std) in reparameterize at eval time

In VarAutoEncoder and VarFullyConnectedNet, the reparameterize method
computed  and always returned .
At training time,  was correctly replaced with random noise scaled
by the standard deviation. But at eval time, the raw standard deviation
was still added to the mean — giving  instead of just .

The reparameterization trick (Kingma & Welling, 2014) defines:
  - Training:  z = μ + ε · σ  (ε ~ N(0,1))
  - Inference: z = μ           (no stochastic component)

This fix restructures the method to return  directly when not
training, avoiding the unnecessary  computation
and eliminating the incorrect  result at inference.

Fixes #8413

Signed-off-by: Raphael Malikian <rtmalikian@gmail.com>

Author: rtmalikian

monai/networks/nets/fullyconnectednet.py

@@ -172,12 +172,11 @@ def decode_forward(self, z: torch.Tensor, use_sigmoid: bool = True) -> torch.Ten
         return x
 
     def reparameterize(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:
-        std = torch.exp(0.5 * logvar)
+        if self.training:  # reparameterization trick only during training
+            std = torch.exp(0.5 * logvar)
+            return mu + torch.randn_like(std) * std
 
-        if self.training:  # multiply random noise with std only during training
-            std = torch.randn_like(std).mul(std)
-
-        return std.add_(mu)
+        return mu
 
     def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         mu, logvar = self.encode_forward(x)

monai/networks/nets/varautoencoder.py

@@ -142,12 +142,11 @@ def decode_forward(self, z: torch.Tensor, use_sigmoid: bool = True) -> torch.Ten
         return x
 
     def reparameterize(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:
-        std = torch.exp(0.5 * logvar)
+        if self.training:  # reparameterization trick only during training
+            std = torch.exp(0.5 * logvar)
+            return mu + torch.randn_like(std) * std
 
-        if self.training:  # multiply random noise with std only during training
-            std = torch.randn_like(std).mul(std)
-
-        return std.add_(mu)
+        return mu
 
     def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         mu, logvar = self.encode_forward(x)