diff --git a/language_interpolation/state_space_network.py b/language_interpolation/state_space_network.py
index 4608918..5d0528f 100644
--- a/language_interpolation/state_space_network.py
+++ b/language_interpolation/state_space_network.py
@@ -31,7 +31,7 @@
 from dataclasses import dataclass
 from einops import rearrange, repeat, einsum
 from typing import Union
-
+from language_interpolation.utils import reshape_apply
 
 # TODO: I don't like this approach to setting data inputs
 @dataclass
@@ -194,8 +194,8 @@ class Mamba, https://github.com/state-spaces/mamba/blob/main/mamba_ssm/modules/m
         
         y = y * F.silu(res)
         
-        output = self.out_proj(y)
-
+        #output = self.out_proj(y)
+        output = reshape_apply(y, self.out_proj)
         return output
 
     
diff --git a/language_interpolation/utils.py b/language_interpolation/utils.py
index a427c15..67d94c2 100644
--- a/language_interpolation/utils.py
+++ b/language_interpolation/utils.py
@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Any
 
 from high_order_layers_torch.layers import *
 from pytorch_lightning import Callback
@@ -17,6 +17,23 @@
 
 logger = logging.getLogger(__name__)
 
+def reshape_apply(x : Tensor, layer: Any) :
+    """
+    TODO: Move this to high order layers torch
+    Linear layer works on arbitrary shaped tensors, but the
+    High Order Layers do not, so this just solves it for the second
+    case, but also works for the first
+    Args:
+        x : The tensor that needs to be reshaped
+        layer: The layer to apply to
+    Returns:
+        output
+    """
+    shape = x.shape
+    last = shape[-1]
+    first = torch.prod(torch.tensor(shape[:-1]))
+    flatout : Tensor = layer(x.view(first, last))
+    return flatout.view(*shape[:-1],-1)
 
 def create_gutenberg_cache(parent_directory: str):
     """