[Fix] : Correct Softmax Gradient#31
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PrimedErwin merged 4 commits intopocketpy:testfrom Aug 1, 2025
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- This is no fix afterall...but yeah it looks good now, dont judge my career on the basis of this commit later on :(
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This PR fixes a tricky bug in our softmax backwards pass, where the gradients weren't matching up with PyTorch Results.
Fixed GradFn_softmax to ensure it correctly computes the full Jacobian-vector product and Tensor_backward to recognise "Softmax" as a special case. Now, it knows to use the gradient from GradFn_softmax directly, skipping the extra multiplication