{
  "schemaVersion": 1,
  "lessonId": "transformers-02",
  "contentHash": "d62730a9b6f73ecf78a5b1ea0d799061469f8f60316f7715e6cd70ed70a0f606",
  "executedAt": "2026-10-20T10:47:08.194358+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Causal means: [2.       3.       4.666667 7.5     ]\nCausal means: [2.       3.       4.666667 7.5     ]\nPASS: transformers-02\n",
  "notebookCells": [
    {
      "sourceHash": "7e5ee5ec7517181c7e3f61894dd9cbe81449404b1460729405e4c4251be8abd2",
      "outputs": []
    },
    {
      "sourceHash": "cc443b31ab17557126954129bca0eb864e06379614edc3208aa8f67c6b4d15b0",
      "outputs": []
    },
    {
      "sourceHash": "0f6543ecc74ba959f25edef09711dd4bbb9430ca25ad2834ce08977d301cdd6f",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Causal means: [2.       3.       4.666667 7.5     ]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "7e376540cb77e259128a0f915a0d3852a2b8b2d4f32e0d932bafe41b6512b02f",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Causal means: [2.       3.       4.666667 7.5     ]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "3772588cbd2e6949186f7f2b38cb69e855df880a71a3cb35801697785b99a788",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "fec12a3d4293943cb60f53dc94330a39b72461d25527dfa2392f35abd0468560",
      "outputs": []
    },
    {
      "sourceHash": "9a2836e3a55186eab7294470ae18384bce208ab56836e86f1a7f9f27e102737c",
      "outputs": []
    },
    {
      "sourceHash": "56bad1118da1b770a0fa237eeb83b9f3193ed9a39e360d4dc12a9dd8e6bbfe90",
      "outputs": []
    },
    {
      "sourceHash": "34d484a183ea724fa0ede3549cda2a1dec35f471629a78dab8d9678dd9eb36e1",
      "outputs": []
    },
    {
      "sourceHash": "edd52ef701ac694a28c4b604455a1be069c98c33663cd0e05b7a0abfb61c8eb8",
      "outputs": []
    },
    {
      "sourceHash": "3152b6c0952edc79416e04d502e3c4ea8b42627d09eaeae7953a5e3dd91509c5",
      "outputs": []
    },
    {
      "sourceHash": "51b5ef68cc387a6a5a7466b2a8d46eb65ac9d6c0e66df26579e2bd8dbf42c2d7",
      "outputs": []
    },
    {
      "sourceHash": "a73bf1190726ffaeca8106154caa29275680ac682432f52342a4e793f7d4a896",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}