{
  "schemaVersion": 1,
  "lessonId": "arrays-04",
  "contentHash": "c29171d688888a8baa96c70ad58fb86f9daaf8c58b12cceed84e79375ed6a3c8",
  "executedAt": "2026-10-20T10:45:15.394008+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Original: [1. 2. 3. 4.]\nSet: [ 1. 20.  3.  4.]\nAdd: [6. 2. 8. 4.]\nPASS: arrays-04\n",
  "notebookCells": [
    {
      "sourceHash": "cd561bfeda119d8609fb44416599e37faf3f5b4c93f5b59f4258371752a6be12",
      "outputs": []
    },
    {
      "sourceHash": "70d3eda86089f7fe011981033ba5dd8ca4646ef46cc631d93b87e11e9e52174c",
      "outputs": []
    },
    {
      "sourceHash": "e69af798bdda1115b7bf563561fb84dd7cdfe32e5ffd0f9fc17fc1e704f1de72",
      "outputs": []
    },
    {
      "sourceHash": "f2ab6aa415eb25bff1219bc1303be8a8770edc1990c0df65423a829addb4c43d",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original: [1. 2. 3. 4.]\n",
            "Set: [ 1. 20.  3.  4.]\n",
            "Add: [6. 2. 8. 4.]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "8357c0eaad31dd00ea73b4c4ffc5b046b2747481ff56a922c240786682e80c35",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "fda4530890b7e8095fada50db4c2fe932b18e45c24b8d477c30c168f2b49a5e4",
      "outputs": []
    },
    {
      "sourceHash": "57f15ead63e17eff387e515fbdabe4e6df029d6f276859aeeafca585812f9673",
      "outputs": []
    },
    {
      "sourceHash": "959b81a683df4d78f3210e7f42e6104fb442c7737bc1d769fb55f9ec97df19c9",
      "outputs": []
    },
    {
      "sourceHash": "9e7fbfe78a96cea3fc3da93c9226ce016f91b0651ca8c6e00fa0ba9c4a407f41",
      "outputs": []
    },
    {
      "sourceHash": "6dda8251b46b791da84907629902077951635cc620842135dbe710ca98193f73",
      "outputs": []
    },
    {
      "sourceHash": "92ce79c804bbd02e1812681855e0bdbb6947c5776015337165ef31aad13a4c3e",
      "outputs": []
    },
    {
      "sourceHash": "37c9a220ab52c9ebf36af7326ff01b63d3e90824463698b888b77c92030161e8",
      "outputs": []
    },
    {
      "sourceHash": "f5cc79429afe966ad6472340806ff9bad2fb0810c379634a5c85d5fd9808208c",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}