{
  "schemaVersion": 1,
  "lessonId": "welcome-01",
  "contentHash": "f15bc8e6b9ae9d88474253a7e4224a1c0d7f398f5a98b3f18f76fcbbdd5ef7e0",
  "executedAt": "2026-10-20T10:45:05.346444+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Python: 3.14.3\nJAX: 0.9.2\nNumPy: 2.4.4\nBackend: cpu\nDevices: [CpuDevice(id=0)]\nSum: 6.0\nChanged experiment sum: 15.0\nPASS: welcome-01\n",
  "notebookCells": [
    {
      "sourceHash": "3181195c9288555b6db37efaaff861c7c5e01458334c2b2b26e58185c240c98e",
      "outputs": []
    },
    {
      "sourceHash": "03214a36adabf9eaf5ff9b8ed0b88a8bbe8f18a4c7e92ee8a8e87f4a8f5cd96a",
      "outputs": []
    },
    {
      "sourceHash": "08fe39714b6766d4b2ac04d4f7b1943cfad945fc1ec43e6a21d6bb2c9f2d62b0",
      "outputs": []
    },
    {
      "sourceHash": "0231471e4c47f87f7721ddaf17c6e6b20e46712eeebfaa0954f95d08b1bd5671",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python: 3.14.3\n",
            "JAX: 0.9.2\n",
            "NumPy: 2.4.4\n",
            "Backend: cpu\n",
            "Devices: [CpuDevice(id=0)]\n",
            "Sum: 6.0\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "3a1394b1cc79bd8d5dfd003c8e992e3df2dfe5be2d354b75b98367906d52dff3",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "bb32c39b2ce96211b55d21d8c9cca7eaa910a9fa9c21a64854d6dd9dadc0688f",
      "outputs": []
    },
    {
      "sourceHash": "a40451c3d44c9a6817316790ed611a177917866f873580496cf21099e5b5f897",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Changed experiment sum: 15.0\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}