{
  "schemaVersion": 1,
  "lessonId": "welcome-03",
  "contentHash": "c635ba5492b7276fae2ed1af1cbef264ceb432ae40c49d9b40291f8eb84821ac",
  "executedAt": "2026-10-20T10:45:09.080862+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "{\n  \"expected\": \"cpu\",\n  \"output_devices\": [\n    \"TFRT_CPU_0\"\n  ],\n  \"platform\": \"cpu\",\n  \"jax\": \"0.9.2\",\n  \"python\": \"3.14.3\",\n  \"shape\": [\n    8\n  ],\n  \"dtype\": \"float32\",\n  \"max_absolute_error\": 0.0,\n  \"process_index\": 0,\n  \"process_count\": 1\n}\nCompleted prediction: [0.34375, 0.8125, 1.28125, 1.75, 2.21875, 2.6875, 3.15625, 3.625]\n{\n  \"expected\": \"cpu\",\n  \"output_devices\": [\n    \"TFRT_CPU_0\"\n  ],\n  \"platform\": \"cpu\",\n  \"jax\": \"0.9.2\",\n  \"python\": \"3.14.3\",\n  \"shape\": [\n    8\n  ],\n  \"dtype\": \"float32\",\n  \"max_absolute_error\": 0.0,\n  \"process_index\": 0,\n  \"process_count\": 1\n}\nCompleted prediction: [0.34375, 0.8125, 1.28125, 1.75, 2.21875, 2.6875, 3.15625, 3.625]\n{\n  \"expected\": \"cpu\",\n  \"output_devices\": [\n    \"TFRT_CPU_0\"\n  ],\n  \"platform\": \"cpu\",\n  \"jax\": \"0.9.2\",\n  \"python\": \"3.14.3\",\n  \"shape\": [\n    8\n  ],\n  \"dtype\": \"float32\",\n  \"max_absolute_error\": 0.0,\n  \"process_index\": 0,\n  \"process_count\": 1\n}\nCompleted prediction: [0.34375, 0.8125, 1.28125, 1.75, 2.21875, 2.6875, 3.15625, 3.625]\nMismatched platform report rejected.\nChanged bias, first prediction: -0.03125\nRow reversal preserved values and requested placement.\nPASS: welcome-03\n",
  "notebookCells": [
    {
      "sourceHash": "a9527639b019311aba343e37bad9eeab6f7717d0a7ec2a9bfef64808233a19fc",
      "outputs": []
    },
    {
      "sourceHash": "f3b5abacd425e8f20953376183f97ffa3ec831659e21f99e3befad1438d2e2e4",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"expected\": \"cpu\",\n",
            "  \"output_devices\": [\n",
            "    \"TFRT_CPU_0\"\n",
            "  ],\n",
            "  \"platform\": \"cpu\",\n",
            "  \"jax\": \"0.9.2\",\n",
            "  \"python\": \"3.14.3\",\n",
            "  \"shape\": [\n",
            "    8\n",
            "  ],\n",
            "  \"dtype\": \"float32\",\n",
            "  \"max_absolute_error\": 0.0,\n",
            "  \"process_index\": 0,\n",
            "  \"process_count\": 1\n",
            "}\n",
            "Completed prediction: [0.34375, 0.8125, 1.28125, 1.75, 2.21875, 2.6875, 3.15625, 3.625]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "e72e7c2326e7afa8502ce6d8d061f79b94e5a3f26e57ab2a88d1540fcdc25277",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"expected\": \"cpu\",\n",
            "  \"output_devices\": [\n",
            "    \"TFRT_CPU_0\"\n",
            "  ],\n",
            "  \"platform\": \"cpu\",\n",
            "  \"jax\": \"0.9.2\",\n",
            "  \"python\": \"3.14.3\",\n",
            "  \"shape\": [\n",
            "    8\n",
            "  ],\n",
            "  \"dtype\": \"float32\",\n",
            "  \"max_absolute_error\": 0.0,\n",
            "  \"process_index\": 0,\n",
            "  \"process_count\": 1\n",
            "}\n",
            "Completed prediction: [0.34375, 0.8125, 1.28125, 1.75, 2.21875, 2.6875, 3.15625, 3.625]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "bd6afdc638e32d4cb6a57cd20d6a34bebf2dd4f03fb73da78ec56c11403e8b03",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"expected\": \"cpu\",\n",
            "  \"output_devices\": [\n",
            "    \"TFRT_CPU_0\"\n",
            "  ],\n",
            "  \"platform\": \"cpu\",\n",
            "  \"jax\": \"0.9.2\",\n",
            "  \"python\": \"3.14.3\",\n",
            "  \"shape\": [\n",
            "    8\n",
            "  ],\n",
            "  \"dtype\": \"float32\",\n",
            "  \"max_absolute_error\": 0.0,\n",
            "  \"process_index\": 0,\n",
            "  \"process_count\": 1\n",
            "}\n",
            "Completed prediction: [0.34375, 0.8125, 1.28125, 1.75, 2.21875, 2.6875, 3.15625, 3.625]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "947fcfa65f76da524661c4479374536a017be807e4f151e939f3abf6bd8cf5c7",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "c9f492d55b7285a60519ac9b18c9516b60a244b26c0fd25fee4dff541c703b74",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mismatched platform report rejected.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "36edade5b52a166821b2bffe86bb81a796da916d9f24156c2aafcc02e90539f0",
      "outputs": []
    },
    {
      "sourceHash": "5928abcfbe86379bad0d18395152b8a5fed7c6beefed78a9872cce4fd9a1980b",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Changed bias, first prediction: -0.03125\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "dead4e3ad9739fa16823b441600ea75cf9f141b4d14a0d861b1d42f305402530",
      "outputs": []
    },
    {
      "sourceHash": "c2f04dd9582bc746b4db8942e103939c8d7423be945a6b4a3942fa3bb239b71c",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row reversal preserved values and requested placement.\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}
