{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.11.0"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample Notebook\n",
    "\n",
    "This is a sample Jupyter notebook for testing anytomd conversion."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "def greet(name):\n",
    "    # CJK comment: \ud55c\uad6d\uc5b4 \u4e2d\u6587 \u65e5\u672c\u8a9e\n",
    "    return f\"Hello, {name}! \ud83d\ude80\""
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "'Hello, World! \ud83d\ude80'"
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Processing\n",
    "\n",
    "Below we process some data."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "data = [1, 2, 3, 4, 5]\n",
    "total = sum(data)\n",
    "print(f\"Total: {total}\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Total: 15\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This is raw text that should be in a plain fenced block."
   ]
  }
 ]
}
