Rebase #91
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Rebase #91
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The implementation is a bit simpler than numpy: we do not have the notion of ufunc loop types (np.add.types etc), so we just cast input tensors to the `result_type(dtype, out.dtype)`, and ask pytorch to do computations in that dtype.
matmul(x1, x2, out) does not broadcast x1, x2 against out, like other ufuncs do.
Note that NumPy does not support in-place __imatmul__, but PyTorch does. So do we then.
This was referenced Mar 23, 2023
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gh-84, gh-88, gh-89, gh-90