Implement the Cholesky decomposition

Doing the Cholesky decomposition with `Tensors.jl` is significantly slower than using `StaticArrays.jl`, presumably because there is no specific code and everything is converted into a standard `Matrix`:

```julia-repl
julia> using Tensors, StaticArrays, LinearAlgebra, BenchmarkTools

julia> A = SymmetricTensor{2,2,Float64}((1.0, 2.0, 5.0))
2×2 SymmetricTensor{2, 2, Float64, 3}:
1.0 2.0
2.0 5.0

julia> B = SMatrix{2,2}(1.0, 2.0, 2.0, 5.0)
2×2 SMatrix{2, 2, Float64, 4} with indices SOneTo(2)×SOneTo(2):
1.0 2.0
2.0 5.0

julia> @benchmark cholesky($A)
BenchmarkTools.Trial: 10000 samples with 992 evaluations per sample.
Range (min … max): 38.306 ns … 3.228 μs ┊ GC (min … max): 0.00% … 98.18%
Time (median): 43.557 ns ┊ GC (median): 0.00%
Time (mean ± σ): 46.372 ns ± 71.473 ns ┊ GC (mean ± σ): 6.35% ± 4.48%

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38.3 ns Histogram: frequency by time 55.4 ns <

Memory estimate: 112 bytes, allocs estimate: 2.

julia> @benchmark cholesky($B)
BenchmarkTools.Trial: 10000 samples with 1000 evaluations per sample.
Range (min … max): 1.708 ns … 13.750 ns ┊ GC (min … max): 0.00% … 0.00%
Time (median): 1.792 ns ┊ GC (median): 0.00%
Time (mean ± σ): 1.864 ns ± 0.199 ns ┊ GC (mean ± σ): 0.00% ± 0.00%

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1.71 ns Histogram: log(frequency) by time 2.12 ns <

Memory estimate: 0 bytes, allocs estimate: 0.
```