⚡ Bolt: Optimize scalar reductions in linalg

[teerthsharma]

💡 What: Replaced high-level operations like .sub() and .mul() within scalar reduction algorithms (e.g. mse, mae, euclidean_distance) with inline loops executing over .data.borrow() array data iteratively in a single pass. Also replaced risky .min() truncations with assert_eq!(a.shape, b.shape).
🎯 Why: To improve execution efficiency across math algorithms. Re-allocating dynamic Tensor structures built on heap arrays (Rc<RefCell<Vec<f64>>>) causes unnecessary and expensive performance impacts. Also ensuring shape equality prevents silent failures.
📊 Impact: Increases computational speed in calculations along the hottest backpropagation and forward paths by minimizing large amounts of implicit intermediate data arrays.
🔬 Measurement: Verify tests run properly, profiling functions internally to verify fewer heap allocations occur in scalar metrics.

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PR created automatically by Jules for task 4963484160542142447 started by @teerthsharma

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