diff --git a/ch03/01_main-chapter-code/ch03.ipynb b/ch03/01_main-chapter-code/ch03.ipynb
index 717af34..1de293c 100644
--- a/ch03/01_main-chapter-code/ch03.ipynb
+++ b/ch03/01_main-chapter-code/ch03.ipynb
@@ -1544,48 +1544,7 @@
    "id": "193d3d2b-2578-40ba-b791-ea2d49328e48",
    "metadata": {},
    "source": [
-    "- In the implementation above, the embedding dimension is 4, because we `d_out=2` as the embedding dimension for the key, query, and value vectors as well as the context vector. And since we have 2 attention heads, we have the output embedding dimension 2*2=4.\n",
-    "\n",
-    "- If we want to have an output dimension of 2, as earlier in single-head attention, we can have to change the projection dimension `d_out` to 1:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "dc9a4375-068b-4b2a-aabb-a29347ca5ecd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[[-0.5740,  0.2216],\n",
-      "         [-0.7320,  0.0155],\n",
-      "         [-0.7774, -0.0546],\n",
-      "         [-0.6979, -0.0817],\n",
-      "         [-0.6538, -0.0957],\n",
-      "         [-0.6424, -0.1065]],\n",
-      "\n",
-      "        [[-0.5740,  0.2216],\n",
-      "         [-0.7320,  0.0155],\n",
-      "         [-0.7774, -0.0546],\n",
-      "         [-0.6979, -0.0817],\n",
-      "         [-0.6538, -0.0957],\n",
-      "         [-0.6424, -0.1065]]], grad_fn=<CatBackward0>)\n",
-      "context_vecs.shape: torch.Size([2, 6, 2])\n"
-     ]
-    }
-   ],
-   "source": [
-    "torch.manual_seed(123)\n",
-    "\n",
-    "d_out = 1\n",
-    "mha = MultiHeadAttentionWrapper(d_in, d_out, block_size, 0.0, num_heads=2)\n",
-    "\n",
-    "context_vecs = mha(batch)\n",
-    "\n",
-    "print(context_vecs)\n",
-    "print(\"context_vecs.shape:\", context_vecs.shape)"
+    "- In the implementation above, the embedding dimension is 4, because we `d_out=2` as the embedding dimension for the key, query, and value vectors as well as the context vector. And since we have 2 attention heads, we have the output embedding dimension 2*2=4."
    ]
   },
   {
@@ -1865,7 +1824,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,