{
  "schemaVersion": 1,
  "lessonId": "transforms-04",
  "contentHash": "2bde24a4e956087fd03b0c6123d05749b5932e233dfd3164bb8d2674884aa209",
  "executedAt": "2026-10-20T10:45:23.194590+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Loss: 0.46000000834465027\nFirst/repeat seconds: 0.013900000136345625 1.920759677886963e-05\neager median / min / max seconds: 2.6375288143754005e-05 2.3334287106990814e-05 7.895799353718758e-05\ncompiled median / min / max seconds: 4.312489181756973e-06 3.874767571687698e-06 5.416106432676315e-06\nbackend / shapes / dtype: cpu (4, 3) (3,) (4,) float32\ncompiled value-and-grad median seconds: 6.770249456167221e-06\nPASS: transforms-04\n",
  "notebookCells": [
    {
      "sourceHash": "2e5c35b62cbdc60f40a90674e91b2dd9d38d5dbd833b175721a1070426836eaa",
      "outputs": []
    },
    {
      "sourceHash": "b788e8fe92bdbcb8a56af07053b761ac37ee875a927f0b83ac4f392f3b2da918",
      "outputs": []
    },
    {
      "sourceHash": "bdf3943bc2cd0fe745537609f625b965e5e07dd37e7972c28ffd2b1e0d769765",
      "outputs": []
    },
    {
      "sourceHash": "3f8fdee1add7294f89c8332bc24fe09694f97c4e00246ca1e44b1c61c84399c8",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 0.46000000834465027\n",
            "First/repeat seconds: 0.013769499957561493 1.9249971956014633e-05\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "0b389274c92d886af03f86fe3b2d2428bfda544c504b4eb4754acb71001d2a81",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "2a0773bce72daa407f6f79d4123aebbdfc5b6f40d18a63e6a4e53513022aeda7",
      "outputs": []
    },
    {
      "sourceHash": "2aa9333814ecf4c63fe728f8d6e15cf90581f599606739f1006137d18f5f0290",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "eager median / min / max seconds: 2.2624386474490166e-05 2.1582935005426407e-05 3.470759838819504e-05\n",
            "compiled median / min / max seconds: 3.7499703466892242e-06 3.582797944545746e-06 4.875008016824722e-06\n",
            "backend / shapes / dtype: cpu (4, 3) (3,) (4,) float32\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "0f58fd5f884533abe8cb256b165955561b0ce11658c769dee00f343d5a012d07",
      "outputs": []
    },
    {
      "sourceHash": "f31932adcd3dac4754ea2f5eef6230dd7fa95dac9be32fa6c0fe51ddc72a88f0",
      "outputs": []
    },
    {
      "sourceHash": "c5514b388e5c3e3b5c3404378d06ce42943cb02f617b57bc08102f20b34253d7",
      "outputs": []
    },
    {
      "sourceHash": "20da53fa4a1ee66d8c044cc2a21d8dbc9a6b84162c4f6c266f921e3d1853e1e0",
      "outputs": []
    },
    {
      "sourceHash": "f13e0af89c2640b98012419559f80102b071795f2f93d8f9e9e0b8a475a1317f",
      "outputs": []
    },
    {
      "sourceHash": "c61e7aaa8cc5f59004a30c33407e5dd23b87c9090b2790a34034b33cfc4326c0",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "compiled value-and-grad median seconds: 6.4999330788850784e-06\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}