{
  "schemaVersion": 1,
  "lessonId": "distributed-01",
  "contentHash": "099854f4a238be06a63c9181a954d2abd88a02db6567e62e94e58a2c2852e3c1",
  "executedAt": "2026-10-20T10:47:44.700275+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 4,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Row-shard shapes: [(2, 4), (2, 4), (2, 4), (2, 4)]\nColumn-shard shapes: [(8, 1), (8, 1), (8, 1), (8, 1)]\nColumn sums: [112. 120. 128. 136.]\nRow-shard shapes: [(2, 4), (2, 4), (2, 4), (2, 4)]\nColumn-shard shapes: [(8, 1), (8, 1), (8, 1), (8, 1)]\nColumn sums: [112. 120. 128. 136.]\nResharded values preserved\nRow sums: [  6.  22.  38.  54.  70.  86. 102. 118.]\nExpected indivisible batch\nExpected indivisible batch\nPASS: distributed-01\n",
  "notebookCells": [
    {
      "sourceHash": "538eb5b487dd2eff1115f4f2758cb069da750dd853180a765c6d2a3f1bd8c54b",
      "outputs": []
    },
    {
      "sourceHash": "3fad90fca57ddab07c932dc8f59a66b7091390c9bcf59b2926227bc5bb0940ee",
      "outputs": []
    },
    {
      "sourceHash": "345a75384a78e9ea2302c68bac72e66e22035187164c7a03b52c29a89d6445d4",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row-shard shapes: [(2, 4), (2, 4), (2, 4), (2, 4)]\n",
            "Column-shard shapes: [(8, 1), (8, 1), (8, 1), (8, 1)]\n",
            "Column sums: [112. 120. 128. 136.]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "b55c8bfae14e486fad9024b804c4cfd3bdfd00a970864132e5be48a9a13c7ad7",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row-shard shapes: [(2, 4), (2, 4), (2, 4), (2, 4)]\n",
            "Column-shard shapes: [(8, 1), (8, 1), (8, 1), (8, 1)]\n",
            "Column sums: [112. 120. 128. 136.]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "7e327ec1e905ffcd657a9e3da99b715765f4836fd832c7d078e430a2eb96b058",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "3d6ff691ebf719b546ba3fca1a25bbf70dd99b0f4d5e5eeab5f1be019829cf4f",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resharded values preserved\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "d2c62040fe6fce5c34810fe202d29e2e9f4d3039ad6e02069a11903d22633877",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row sums: [  6.  22.  38.  54.  70.  86. 102. 118.]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "925b9c6557c540a4c313f2eac42ae8f1655d9431dbe337d46177908f74ef2e18",
      "outputs": []
    },
    {
      "sourceHash": "d046f34d41fd2b16ab104616ac4f05d90f8952b575d0d05b566b2737560fbd9d",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected indivisible batch\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "97ae6c3e725e367920f868bdc5c8b9fcbc8f471a0cbf8c75891fa4dbc0c9f389",
      "outputs": []
    },
    {
      "sourceHash": "70f01f9aac0467c4f32d1a2f6d95c97d70a9e98db4ff6e31243e4b98cee520ba",
      "outputs": []
    },
    {
      "sourceHash": "ff45720de1fc96835f33157a8d20820f7fec0ad3bb0cd043b69c37a7b47b10fc",
      "outputs": []
    },
    {
      "sourceHash": "fba941daf027d203782fcd19076de969b345cbc8629c0912fb1c66f81690ee12",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected indivisible batch\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}