diff --git a/direct/data/transforms.py b/direct/data/transforms.py
index 05584e2c..c829c290 100644
--- a/direct/data/transforms.py
+++ b/direct/data/transforms.py
@@ -591,6 +591,7 @@ def root_sum_of_squares(data: torch.Tensor, dim: int = 0, complex_dim: int = -1)
     torch.Tensor : RSS of the input tensor.
     """
     if is_complex_data(data):
+
         return torch.sqrt((data ** 2).sum(complex_dim).sum(dim))
 
     return torch.sqrt((data ** 2).sum(dim))
diff --git a/direct/nn/varnet/varnet.py b/direct/nn/varnet/varnet.py
index a1e8fd3f..42209b6d 100644
--- a/direct/nn/varnet/varnet.py
+++ b/direct/nn/varnet/varnet.py
@@ -23,6 +23,7 @@ def __init__(
         regularizer_num_filters: int = 18,
         regularizer_num_pull_layers: int = 4,
         regularizer_dropout: float = 0.0,
+        in_channels: int = 2,
         **kwargs,
     ):
         """
@@ -126,6 +127,7 @@ def __init__(
         self.learning_rate = nn.Parameter(torch.tensor([1.0]))
 
         self._coil_dim = 1
+        self._complex_dim = -1
         self._spatial_dims = (2, 3)
 
     def forward(
@@ -159,12 +161,24 @@ def forward(
             current_kspace - masked_kspace,
         )
 
-        regularization_term = reduce_operator(
-            self.backward_operator(current_kspace, dim=self._spatial_dims), sensitivity_map, dim=self._coil_dim
+        regularization_term = torch.cat(
+            [
+                reduce_operator(
+                    self.backward_operator(kspace, dim=self._spatial_dims), sensitivity_map, dim=self._coil_dim
+                )
+                for kspace in torch.split(current_kspace, 2, self._complex_dim)
+            ],
+            dim=self._complex_dim,
         ).permute(0, 3, 1, 2)
         regularization_term = self.regularizer_model(regularization_term).permute(0, 2, 3, 1)
-        regularization_term = self.forward_operator(
-            expand_operator(regularization_term, sensitivity_map, dim=self._coil_dim), dim=self._spatial_dims
+        regularization_term = torch.cat(
+            [
+                self.forward_operator(
+                    expand_operator(image, sensitivity_map, dim=self._coil_dim), dim=self._spatial_dims
+                )
+                for image in torch.split(regularization_term, 2, self._complex_dim)
+            ],
+            dim=self._complex_dim,
         )
 
         return current_kspace - self.learning_rate * kspace_error + regularization_term