{
  "schemaVersion": 1,
  "lessonId": "kernels-02",
  "contentHash": "59bc73b55f193ea8dbea59a1a0b4aa9ebf1cf8688fa7788d85092e22c27f943c",
  "executedAt": "2026-10-20T10:49:37.641695+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Interpretation max error: 0.0\nZero fraction and endpoint: 0.48578811369509045 2.4000000953674316\nExpected target refusal: Real TPU inputs/backend required; CPU fallback is disabled\nInterpretation max error: 0.0\nZero fraction and endpoint: 0.48578811369509045 2.4000000953674316\nExpected target refusal: Real TPU inputs/backend required; CPU fallback is disabled\nbfloat16 represented-input reference matched exactly\nBoundary fixture: [[0. 0. 0.]\n [2. 0. 0.]]\nChanged tails and two TPU-compatible blocks verified in interpretation\nMalformed bias axes rejected\nColumn/bias pairing survives a joint permutation\nPASS: kernels-02\n",
  "notebookCells": [
    {
      "sourceHash": "6274e0f187a9a8d566769af2b4b84fa11643592fd76344253a57a738322ad39a",
      "outputs": []
    },
    {
      "sourceHash": "f7ba1fb53cf739798995a542bd416d30d0ce33121f527715456205b365581708",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Interpretation max error: 0.0\n",
            "Zero fraction and endpoint: 0.48578811369509045 2.4000000953674316\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "571eef36032fcd4a856b31e6a63d69db4952a56c6e6b656139955bf992454dd2",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected target refusal: Real TPU inputs/backend required; CPU fallback is disabled\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "0e4f35e3e72daa4db8a654caaab8fb48d87ef69b33c5eb0d05da48a42978f439",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Interpretation max error: 0.0\n",
            "Zero fraction and endpoint: 0.48578811369509045 2.4000000953674316\n",
            "Expected target refusal: Real TPU inputs/backend required; CPU fallback is disabled\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "e8e83f5ed51cbfa0d1d3da0d47d8aca60a500c1d5f40d1c9da29a98ae3fa9f0b",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "47aa8ff41a765156592fdb1db29d78046d8fb07d54d96ec90d63ff405dc77644",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bfloat16 represented-input reference matched exactly\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "a0238c731bbcd78c1757616e589a5f4d345c9597e34d915d80ee5fae35a9e91f",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Boundary fixture: [[0. 0. 0.]\n",
            " [2. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "3cadb36ae5dd937434721579699bd38cc77510c20a68926241f712ca0988402a",
      "outputs": []
    },
    {
      "sourceHash": "548f05976df751e1f106281962403bfd7090ff97f70845455fecd416a12e50b7",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Changed tails and two TPU-compatible blocks verified in interpretation\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "046efa031433f74da9c0e58db5d9e1e4e57cc670391948acedc89f73cd561aae",
      "outputs": []
    },
    {
      "sourceHash": "17a3a84fc99f513081f025f4ad4b827b6bf37f41f9fea60e5970ff9166115082",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Malformed bias axes rejected\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "d5d625c5dcc7187cfb4ebb808a16c50a816382a955877d19948b70722107b152",
      "outputs": []
    },
    {
      "sourceHash": "d10789a5f7526a54e60d4420bbf0167f27f012c6cdf0241bfdc1dc8d992c394e",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column/bias pairing survives a joint permutation\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}
