{
  "schemaVersion": 1,
  "lessonId": "internals-01",
  "contentHash": "4605e7019f566f40efc6dba64e28ee6d18610e761aabe9e5eb3e84c46d3896c2",
  "rendererHash": "a1bd5b648f4731ae5d5f0a24811c2540d1a0a132149ce589d17c4113509cbedf",
  "kind": "executed",
  "generatedAt": "2026-10-20T10:46:35.063790+00:00",
  "provenance": "recorded_cpu_computation",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Forward program:\n{ lambda ; a:f32[3]. let\n    b:f32[3] = add a 1.0:f32[]\n    c:f32[3] = mul b b\n    d:f32[] = reduce_sum[axes=(0,) out_sharding=None] c\n  in (d,) }\nOperation: add output shapes: [(3,)]\nOperation: mul output shapes: [(3,)]\nOperation: reduce_sum output shapes: [()]\nValue / gradient: 29.0 [4. 6. 8.]\n",
  "data": {
    "kind": "heatmap",
    "values": [
      [
        1.0,
        2.0,
        3.0
      ],
      [
        2.0,
        3.0,
        4.0
      ],
      [
        4.0,
        9.0,
        16.0
      ]
    ],
    "rows": [
      "input",
      "add 1",
      "multiply by self"
    ],
    "columns": [
      "coordinate 0",
      "coordinate 1",
      "coordinate 2"
    ],
    "unit": "intermediate value"
  }
}