[decimal] Implement root() function to calculate the n-th root of a Decimal value

[forfudan]

This pull request introduces the root function to the DeciMojo library, which calculates the n-th root of a Decimal value using the Newton-Raphson method. The most important changes include adding the root function, updating the Decimal struct to include the root method, and adding comprehensive tests for the new functionality.

New Functionality:

• src/decimojo/exponential.mojo: Added the root function to calculate the n-th root of a Decimal value. This function includes special cases for handling different values of n and x, and uses the Newton-Raphson method for iteration.

Updates to Existing Code:

• src/decimojo/decimal.mojo: Updated the Decimal struct to include the root method, which calls the new root function.
• src/decimojo/__init__.mojo: Updated the import statements to include the new root function from the exponential module.

Testing:

• tests/test_root.mojo: Added comprehensive tests for the root function, covering basic root calculations, fractional inputs, edge cases, error conditions, precision, and mathematical identities.

Configuration:

• mojoproject.toml: Added commands for testing and benchmarking the new root function.

[forfudan]

=== DeciMojo Root Function Benchmark ===
Time: 2025-03-18T20:42:17.914611
System: Darwin 24.3.0
Processor: arm
Python version: 3.12.9
Python decimal precision: 28
Mojo decimal precision: 28

Running root function benchmarks with 100 iterations each

Benchmark:       Square root of perfect square
Value:           9
Root:            2
Mojo result:     3
Python result:   3.000000000000000000000000000
Mojo root():     40.0 ns per iteration
Python root():   47910.0 ns per iteration
Speedup factor:  1197.75

Benchmark:       Square root of non-perfect square
Value:           2
Root:            2
Mojo result:     1.4142135623730950488016887242
Python result:   1.414213562373095048801688724
Mojo root():     1950.0 ns per iteration
Python root():   45900.0 ns per iteration
Speedup factor:  23.53846153846154

Benchmark:       Cube root of perfect cube
Value:           8
Root:            3
Mojo result:     2
Python result:   2.000000000000000000000000000
Mojo root():     550.0 ns per iteration
Python root():   47890.0 ns per iteration
Speedup factor:  87.07272727272728

Benchmark:       Cube root of non-perfect cube
Value:           10
Root:            3
Mojo result:     2.1544346900318837217592935665
Python result:   2.154434690031883721759293566
Mojo root():     2080.0 ns per iteration
Python root():   16960.0 ns per iteration
Speedup factor:  8.153846153846153

Benchmark:       Fourth root of perfect power
Value:           16
Root:            4
Mojo result:     2
Python result:   2.000000000000000000000000000
Mojo root():     760.0 ns per iteration
Python root():   47190.0 ns per iteration
Speedup factor:  62.0921052631579

Benchmark:       Fifth root of perfect power
Value:           32
Root:            5
Mojo result:     2
Python result:   2.000000000000000000000000000
Mojo root():     810.0 ns per iteration
Python root():   48090.0 ns per iteration
Speedup factor:  59.370370370370374

Benchmark:       Root of decimal < 1
Value:           0.25
Root:            2
Mojo result:     0.5
Python result:   0.5000000000000000000000000000
Mojo root():     40.0 ns per iteration
Python root():   44700.0 ns per iteration
Speedup factor:  1117.5

Benchmark:       Root of small decimal
Value:           0.0625
Root:            4
Mojo result:     0.5
Python result:   0.5000000000000000000000000000
Mojo root():     7000.0 ns per iteration
Python root():   45950.0 ns per iteration
Speedup factor:  6.564285714285714

Benchmark:       High precision decimal
Value:           2.7182818284590452353602874
Root:            2
Mojo result:     1.6487212707001281468486507662
Python result:   1.648721270700128146848650766
Mojo root():     1560.0 ns per iteration
Python root():   44670.0 ns per iteration
Speedup factor:  28.634615384615383

Benchmark:       Large integer
Value:           1000000
Root:            2
Mojo result:     1000
Python result:   1000.000000000000000000000000
Mojo root():     50.0 ns per iteration
Python root():   17260.0 ns per iteration
Speedup factor:  345.2

Benchmark:       Large root
Value:           10
Root:            100
Mojo result:     1.0232929922807541309662751747
Python result:   1.023292992280754130966275175
Mojo root():     3670.0 ns per iteration
Python root():   11330.0 ns per iteration
Speedup factor:  3.087193460490463

Benchmark:       Odd root of negative number
Value:           -27
Root:            3
Note: Python doesn't directly support odd roots of negative numbers.
      Using abs() and then negating the result for comparison.
Mojo result:     -3
Python result:   -3.000000000000000000000000000
Mojo root():     530.0 ns per iteration
Python root():   50050.0 ns per iteration
Speedup factor:  94.43396226415095

Benchmark:       Root of 1 (any root)
Value:           1
Root:            7
Mojo result:     1
Python result:   1.000000000000000000000000000
Mojo root():     10.0 ns per iteration
Python root():   3830.0 ns per iteration
Speedup factor:  383.0

Benchmark:       Root of 0
Value:           0
Root:            3
Mojo result:     0
Python result:   0
Mojo root():     10.0 ns per iteration
Python root():   3870.0 ns per iteration
Speedup factor:  387.0

Benchmark:       Custom decimal
Value:           123.456
Root:            2
Mojo result:     11.111075555498666484621494041
Python result:   11.11107555549866648462149404
Mojo root():     1450.0 ns per iteration
Python root():   45590.0 ns per iteration
Speedup factor:  31.44137931034483

=== Root Function Benchmark Summary ===
Benchmarked:      15 different root() cases
Each case ran:    100 iterations
Average speedup:  255.65592978216335×

Individual speedup factors:
Case 1: 1197.75×
Case 2: 23.54×
Case 3: 87.07×
Case 4: 8.15×
Case 5: 62.09×
Case 6: 59.37×
Case 7: 1117.5×
Case 8: 6.56×
Case 9: 28.63×
Case 10: 345.2×
Case 11: 3.09×
Case 12: 94.43×
Case 13: 383.0×
Case 14: 387.0×
Case 15: 31.44×