)]}'
{
  "commit": "ec0026e0bc8b7904b29e167e39b252c7e2794d4a",
  "tree": "3d302aec19324423b22b0ceaa0af853a0b343df0",
  "parents": [
    "d6e63342df38018e2911f89bb888ad872dde9a69"
  ],
  "author": {
    "name": "Guan-Ming (Wesley) Chiu",
    "email": "105915352+guan404ming@users.noreply.github.com",
    "time": "Tue Dec 02 04:21:32 2025 +0800"
  },
  "committer": {
    "name": "GitHub",
    "email": "noreply@github.com",
    "time": "Mon Dec 01 15:21:32 2025 -0500"
  },
  "message": "[Relax][PyTorch] Fix index_put with broadcast indices (#18533)\n\n## Related Issue\n\ncloses https://github.com/apache/tvm/issues/18355\n\n## Why\n\nConverting PyTorch operations like M[:, rows, cols] \u003d x failed because:\n1. The TOPI index_put implementation called len() on TVM Tensor objects\n(unsupported)\n2. Index tensors with different shapes (e.g., (2,) and (10,)) couldn\u0027t\nbroadcast together\n\n## How\n\n- Added broadcasting support following NumPy rules to handle\nmulti-dimensional index tensors\n- add tests for batched indexing pattern M[:, rows, cols] \u003d x",
  "tree_diff": [
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