diff --git "a/benchmark/numpy_5/numpy_5_reproduced.ipynb" "b/benchmark/numpy_5/numpy_5_reproduced.ipynb"
--- "a/benchmark/numpy_5/numpy_5_reproduced.ipynb"
+++ "b/benchmark/numpy_5/numpy_5_reproduced.ipynb"
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-18T11:01:26.617981Z",
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-18T11:01:26.660749Z",
@@ -481,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-18T11:01:39.870224Z",
@@ -507,7 +507,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 4000x2000 with 20 Axes>"
       ]
@@ -519,7 +519,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 8.090335130691528\n"
+      "0 9.450815677642822\n"
      ]
     }
    ],
@@ -562,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-18T11:36:46.772208Z",
@@ -580,7 +580,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-20-04708a74e748>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavetxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'images.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-6-04708a74e748>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavetxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'images.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36msavetxt\u001b[0;34m(fname, X, fmt, delimiter, newline, header, footer, comments, encoding)\u001b[0m\n\u001b[1;32m   1568\u001b[0m         \u001b[0;31m# Handle 1-dimensional arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1569\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1570\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m   1571\u001b[0m                 \"Expected 1D or 2D array, got %dD array instead\" % X.ndim)\n\u001b[1;32m   1572\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: Expected 1D or 2D array, got 5D array instead"
      ]