diff --git a/setup.py b/setup.py
index 31e83b0..7fdb521 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@ with open('README.md') as f:
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.12.1',
+  version = '1.12.2',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description = long_description,
diff --git a/vit_pytorch/vit_nd_rotary.py b/vit_pytorch/vit_nd_rotary.py
index cd814a8..afe3158 100644
--- a/vit_pytorch/vit_nd_rotary.py
+++ b/vit_pytorch/vit_nd_rotary.py
@@ -5,7 +5,7 @@ from torch import nn, arange, cat, stack, Tensor
 from torch.nn import Module, ModuleList
 import torch.nn.functional as F
 
-from einops import rearrange, repeat, reduce
+from einops import rearrange, repeat, reduce, pack, unpack
 from einops.layers.torch import Rearrange
 
 # helpers
@@ -245,7 +245,11 @@ class ViTND(Module):
         self.to_latent = nn.Identity()
         self.mlp_head = nn.Linear(dim, num_classes)
     
-    def forward(self, x):
+    def forward(
+        self,
+        x,
+        return_embed = False
+    ):
         x = self.to_patch_embedding(x) # (b, *spatial_dims, patch_dim)
         
         batch, *spatial_dims, _, device = *x.shape, x.device
@@ -259,16 +263,24 @@ class ViTND(Module):
         # flatten spatial dimensions for attention with nd rotary
         
         pos = repeat(pos, '... p -> b (...) p', b = batch)
-        x = rearrange(x, 'b ... d -> b (...) d')
+        x, packed_shape = pack([x], 'b * d')
 
         x = self.dropout(x)
         
-        x = self.transformer(x, pos)
-        
-        x = reduce(x, 'b n d -> b d', 'mean')
-        
-        x = self.to_latent(x)
-        return self.mlp_head(x)
+        embed = self.transformer(x, pos)
+
+        # return the embed with reconstituted patch shape
+
+        if return_embed:
+            embed, = unpack(embed, packed_shape, 'b * d')
+            return embed
+
+        # pooling to logits
+
+        pooled = reduce(embed, 'b n d -> b d', 'mean')
+
+        pooled = self.to_latent(pooled)
+        return self.mlp_head(pooled)
 
 
 if __name__ == '__main__':
@@ -288,5 +300,7 @@ if __name__ == '__main__':
     )
 
     data = torch.randn(2, 3, 4, 8, 16, 32, 64)
-    
+
     logits = model(data)
+
+    embed = model(data, return_embed = True) # (2, 2, 4, 4, 8, 8, 512)