Unvendor pydoc_data and _pydecimal. ### Description

Removes / unvendors some python modules:

- Remove `_aix_support.py`, which is for supporting IBM AIX OS.

- Unvendor `_pydecimal.py`.
  - _pydecimal is a pure Python implementation of `decimal` module. [Importing `decimal` fallbacks](https://github.com/python/cpython/blob/main/Lib/decimal.py) to `_pydecimal` if the C-implementation `_decimal` is not available. In our case, _decimal is available, so _pydecimal will not be normally used. 

- Unvendor `pydoc_data`.
  - pydoc_data contains [a large (~700KB) dictionary](https://github.com/python/cpython/blob/main/Lib/pydoc_data/topics.py) for explaining python builtins. This is mostly used when `help("...")` is called.

### Checklists

- [x] Add a [CHANGELOG](https://github.com/pyodide/pyodide/blob/main/docs/project/changelog.md) entry
- [x] Add / update tests
- [x] Add new / update outdated documentation

# setup: import numpy as np ; N = 100000 ; a = np.random.random(N); b = 0.1; c =1.1
# run: log_likelihood(a, b, c)
# from: http://arogozhnikov.github.io/2015/09/08/SpeedBenchmarks.html
import numpy

# pythran export log_likelihood(float64[], float64, float64)


def log_likelihood(data, mean, sigma):
    s = (data - mean) ** 2 / (2 * (sigma**2))
    pdfs = numpy.exp(-s)
    pdfs /= numpy.sqrt(2 * numpy.pi) * sigma
    return numpy.log(pdfs).sum()

# setup: import numpy as np ; N = 100000 ; a = np.random.random(N); b = 0.1; c =1.1
# run: log_likelihood(a, b, c)
# from: http://arogozhnikov.github.io/2015/09/08/SpeedBenchmarks.html
import numpy

# pythran export log_likelihood(float64[], float64, float64)


def log_likelihood(data, mean, sigma):
    s = (data - mean) ** 2 / (2 * (sigma**2))
    pdfs = numpy.exp(-s)
    pdfs /= numpy.sqrt(2 * numpy.pi) * sigma
    return numpy.log(pdfs).sum()
