minor kernel cleanups

include/aidge/backend/cpu/operator/PowImpl_backward_kernels.hpp

@@ -37,20 +37,21 @@ void PowImpl_cpu_backward_kernel(const std::vector<std::size_t>& input0Dims,
     I2* grad1 = static_cast<I2*>(gradientInput1_);
     const O* gradOut = static_cast<const O*>(gradOutput_);
 
-
-	auto totalElements = std::accumulate(outputDims.cbegin(), outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+    // Fill input grads with zeros
 	auto input0Elements = std::accumulate(input0Dims.cbegin(), input0Dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
 	std::fill(grad0, grad0 + input0Elements, I1(0));
 	auto input1Elements = std::accumulate(input1Dims.cbegin(), input1Dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
 	std::fill(grad1, grad1 + input1Elements, I1(0));
 
+	auto totalElements = std::accumulate(outputDims.cbegin(), outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
     for (size_t i = 0; i < totalElements; ++i)
     {
+        // Compute indexes in inputs 0 and 1 to support broadcasting
         std::vector<std::size_t> indexes = getMultiDimIndices(outputDims, i);
         std::size_t idx0 = getFlattenedIndex(input0Dims, indexes);
         std::size_t idx1 = getFlattenedIndex(input1Dims, indexes);
 
-        // grad0 = input1 * pow (input0, (input1 -1))
+        // grad0 = grad_output * (input1 * pow(input0, (input1 -1)))
         grad0[idx0] += gradOut[i]*input1[idx1]* std::pow(input0[idx0], input1[idx1]-1);
 
         // grad1 = grad_output * (output * ln(input0))