{
  "schemaVersion": 1,
  "lessonId": "transformers-01",
  "contentHash": "a7197251c1f17b230004129a7936f5275c610353013941c3ed2c8d8d3a0b2346",
  "executedAt": "2026-10-20T10:47:04.943776+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Weights / output: [[0.33333334 0.33333334 0.33333334]\n [0.33333334 0.33333334 0.33333334]] [[2. 2.]\n [2. 2.]]\nWeights / output: [[0.33333334 0.33333334 0.33333334]\n [0.33333334 0.33333334 0.33333334]] [[2. 2.]\n [2. 2.]]\nPASS: transformers-01\n",
  "notebookCells": [
    {
      "sourceHash": "7e5ee5ec7517181c7e3f61894dd9cbe81449404b1460729405e4c4251be8abd2",
      "outputs": []
    },
    {
      "sourceHash": "8a8ca431c38c81aa23eda367daea4910d0002bb7e9c59e3bd40420fa466068ac",
      "outputs": []
    },
    {
      "sourceHash": "914f91836429f7212cfada2c48d70d613f9c7d43a050241496df63f71398e9cc",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weights / output: [[0.33333334 0.33333334 0.33333334]\n",
            " [0.33333334 0.33333334 0.33333334]] [[2. 2.]\n",
            " [2. 2.]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "9d3469198c0c30861ed7582b1eecb5e86d4250cbe5b919faee0787da5ccff25b",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weights / output: [[0.33333334 0.33333334 0.33333334]\n",
            " [0.33333334 0.33333334 0.33333334]] [[2. 2.]\n",
            " [2. 2.]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "ee8e5fae12b979b2277e47e2f1276b47923c7a5d23cd1a9edb4f2c1f6b2c85b3",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "9eff4bbbfeea4f8de1d2e9b6384d69711f531a0faa50928e2e45a07dbb5211bb",
      "outputs": []
    },
    {
      "sourceHash": "dab58eb1c28f5567491b414db9425d065d14782fcfcb6bc5f544e84e77950860",
      "outputs": []
    },
    {
      "sourceHash": "a683a7d4bcda66c7946e084654d5f0a5467c424449bff46c302bc1f4c3bea816",
      "outputs": []
    },
    {
      "sourceHash": "575d427f09e9d40cdf0075ef7d76110889a988418e62a573ed1abdde641702c6",
      "outputs": []
    },
    {
      "sourceHash": "f0a3bc224b3a45af816d3d7a66b141e6980a9c53cd878edf4176a8b60f29a689",
      "outputs": []
    },
    {
      "sourceHash": "1b6589397bfec93dc2b04f63ca1987b9cbbf87c2962cf61fa36557935486e4ef",
      "outputs": []
    },
    {
      "sourceHash": "54736233d826f33ec203a9db614608293359a57afa131f5731c5a2d7aee75625",
      "outputs": []
    },
    {
      "sourceHash": "62bc99b7516585027e58fc58fdbfca035037d8b1a76cd6d02b8291a4b7382ea2",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}