{
  "schemaVersion": 1,
  "lessonId": "networks-02",
  "contentHash": "81334e7f41bd4ff3205536759c986bf631b1fb32e7e2a387c45df4c8f6cbe8f7",
  "executedAt": "2026-10-20T10:46:15.263948+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Trainable scalars: 13; other state: 1; clone counters: 1 2\nTrainable scalars: 13; other state: 1; clone counters: 1 2\nClone logits shifted by one; original parameters unchanged.\nPASS: networks-02\n",
  "notebookCells": [
    {
      "sourceHash": "0c0ed76a3edb6ae6484b56888213859ed41cb7f456f32acb8652503400095c04",
      "outputs": []
    },
    {
      "sourceHash": "4d0e46330f14ab197bc51040ed2ca3b3e496fdd1247b1704e6c52063e309f38d",
      "outputs": []
    },
    {
      "sourceHash": "2bbd32e2249a7fc5d67b378d9fa7affd0177ba6d574bfa894bbb764f7a5811df",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainable scalars: 13; other state: 1; clone counters: 1 2\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "d1cfd4a19b0dedb757fe67b88fdad97671a4c816c2fd35f36e9596eb4c263096",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainable scalars: 13; other state: 1; clone counters: 1 2\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "0023438eda9c4e66e472ea6d2eb58de747c4f6329d6de765c3034729a0fb086b",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "104aa163a20e4d7ad8385abc14e832e7b821b1c4730d626d415ab789fcc493e8",
      "outputs": []
    },
    {
      "sourceHash": "af93c87115da58b657eac16a629589bbcb64b71d54524b1226ba458eb2099654",
      "outputs": []
    },
    {
      "sourceHash": "479fab54e560ef08f41f9aa01df993ed11b6e8342522d4f73dd882ce6a7a6d36",
      "outputs": []
    },
    {
      "sourceHash": "363063eff57f4bd568a8146a17b97141e01dc7334485339a89b16f9b870acb48",
      "outputs": []
    },
    {
      "sourceHash": "dbcb436c31696a33cbfc9e8e8c55ecef488f6aef1b9cc8e023c1ec69351e1e8e",
      "outputs": []
    },
    {
      "sourceHash": "d5c8aa043a0af77b1e3dabf8c067fa8fd6bc44e0ff45dfc69735df67b7914e3a",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clone logits shifted by one; original parameters unchanged.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "d3724f196cd6a5c47699c7452e5f362e51b757644b87bc74de146d4a462d01ea",
      "outputs": []
    },
    {
      "sourceHash": "6f7fcdf3d1537d4a46fd44ad815781a3fca30bb95e296e8501c83f6fd0d444fb",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}