{
  "context": "This criteria evaluates how well the engineer uses CuPy's universal functions (ufuncs) and type promotion capabilities to implement GPU-accelerated element-wise array operations. The focus is exclusively on proper usage of CuPy's API for mathematical operations and array handling.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "CuPy array creation",
      "description": "Uses cupy.array() or cupy.asarray() to create or convert inputs to CuPy arrays on the GPU",
      "max_score": 15
    },
    {
      "name": "Element-wise ufuncs",
      "description": "Uses CuPy's element-wise universal functions (cupy.square or power, cupy.sqrt, cupy.sin, cupy.cos, cupy.add, cupy.multiply) for the mathematical operations rather than implementing custom kernels or loops",
      "max_score": 30
    },
    {
      "name": "Automatic broadcasting",
      "description": "Leverages CuPy's automatic broadcasting for element-wise operations without manual shape manipulation",
      "max_score": 15
    },
    {
      "name": "Type promotion",
      "description": "Relies on CuPy's automatic type promotion rather than explicitly casting types, allowing ufuncs to handle mixed-type arrays naturally",
      "max_score": 20
    },
    {
      "name": "Shape validation",
      "description": "Uses array shape attributes (e.g., array.shape) to validate input shapes and raises appropriate errors",
      "max_score": 10
    },
    {
      "name": "Return GPU arrays",
      "description": "Returns CuPy arrays (GPU arrays) as results rather than converting back to NumPy arrays unnecessarily",
      "max_score": 10
    }
  ]
}