{
  "schemaVersion": 1,
  "lessonId": "arrays-02",
  "contentHash": "28d613b670b5073f1d70575c8995cf41882ab452ee78c3e449b38a9a90c754fb",
  "executedAt": "2026-10-20T10:45:12.332982+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "[[11. 22. 33.]\n [14. 25. 36.]]\nShape: (2, 3) dtype: float32\naligned residuals: [0. 0. 0.]\npairwise residuals: [[ 0.  2.  4.]\n [-2.  0.  2.]\n [-4. -2.  0.]]\nfloat32 large + 1 \u2212 large: 0.0\nPASS: arrays-02\n",
  "notebookCells": [
    {
      "sourceHash": "a88e0e326e0fe072c1e6c8f1017e14af95400fc437ff2e57e9d576d8efdba262",
      "outputs": []
    },
    {
      "sourceHash": "b3857c5ae6c711c557083e6fc46312c454c3ab1eabf48e88a0440d9b60077e67",
      "outputs": []
    },
    {
      "sourceHash": "da1b96a52c5aa40bf4d2fa8846f39239186c613064e802df39dd30783d4445a4",
      "outputs": []
    },
    {
      "sourceHash": "654fd57a59b816bf4c3862a304fd2e1ef0d2446573c9c66b4395ce429158c8fc",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[11. 22. 33.]\n",
            " [14. 25. 36.]]\n",
            "Shape: (2, 3) dtype: float32\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "4e2787d93d634b1a8951d08d6cd84905897359a48143f9a38a24a8f5cf9dcb20",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "d09b12b89c18671acd0ae78bf39be9322b0dcb52fa6ff69d6d4fc1115760ddd4",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "aligned residuals: [0. 0. 0.]\n",
            "pairwise residuals: [[ 0.  2.  4.]\n",
            " [-2.  0.  2.]\n",
            " [-4. -2.  0.]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "a700b55c779fdd97e870604ed559fd485802201cd653c1d7f40d05f30b24c175",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float32 large + 1 \u2212 large: 0.0\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "74f6946b018378e629ce3e150b26706207d661de80837c5a3515f5644b136735",
      "outputs": []
    },
    {
      "sourceHash": "ef41217b547233edc0151ace5d4eca9d31e34d72afc22bbbb42f5a82e756501b",
      "outputs": []
    },
    {
      "sourceHash": "5897f9423bb5de743d9b5f0a3c2f88e1ecfa625f195e2e881437bc2a527a5462",
      "outputs": []
    },
    {
      "sourceHash": "0f9980aeb9f6b7aea48fd065fbe45edc05d1792082755d8af6844b2ac42dd968",
      "outputs": []
    },
    {
      "sourceHash": "a21bec56896c72fe1d24b4aeaf9ed7444f7efa3a5c039cd5f69428bf43fbabc7",
      "outputs": []
    },
    {
      "sourceHash": "578e30e5dcc88cc8beb40d2c80ca3dd834c73bbaea14b8974da9524d06d257fb",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}