results/vectorized_map_runtime_results.json

{
  "artifact": "/workspace/keras_native/hf_keras_native_vectorized_map_poc/vectorized_map_lambda.keras",
  "keras_version": "3.14.1",
  "lambda_paths": [
    "$.config.layers[1].config.function"
  ],
  "marker_after_safe_mode_false_load": true,
  "marker_after_safe_mode_false_load_content": "VECTORIZED_MAP_MARKER",
  "marker_after_safe_mode_true": false,
  "marker_after_unsafe_inference": true,
  "modelscan_issues": [],
  "modelscan_returncode": 0,
  "modelscan_skipped": {
    "skipped_files": [
      {
        "category": "SCAN_NOT_SUPPORTED",
        "description": "Model Scan did not scan file",
        "source": "vectorized_map_lambda.keras:metadata.json"
      },
      {
        "category": "SCAN_NOT_SUPPORTED",
        "description": "Model Scan did not scan file",
        "source": "vectorized_map_lambda.keras:config.json"
      },
      {
        "category": "MODEL_CONFIG",
        "description": "Model Config not found",
        "source": "vectorized_map_lambda.keras:model.weights.h5"
      }
    ],
    "total_skipped": 3
  },
  "modelscan_total_issues": 0,
  "modelscan_version": "0.8.8",
  "safe_mode_false_load": "loaded",
  "safe_mode_true": "blocked:ValueError:Requested the deserialization of a Python lambda. This carries a potential risk of arbitrary code execution and thus it is disallowed by default. If you trust the source of the artifact, you can override this error by passing `safe_mode=False` to the loading function, or calling `keras.config.enable_unsafe_deserialization().",
  "sha256": "536334212fdea8cf9a19cf71d0c4decd74442116eb31ee39aab0c9d28b13757e",
  "unsafe_inference": "ok"
}