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- [x] Add new / update outdated documentation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 = 500000 ; X, Y = np.random.rand(N), np.random.rand(N)
# run: lstsqr(X, Y)
# from:
# http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb

# pythran export lstsqr(float[], float[])
import numpy as np


def lstsqr(x, y):
    """Computes the least-squares solution to a linear matrix equation."""
    x_avg = np.average(x)
    y_avg = np.average(y)
    dx = x - x_avg
    var_x = np.sum(dx**2)
    cov_xy = np.sum(dx * (y - y_avg))
    slope = cov_xy / var_x
    y_interc = y_avg - slope * x_avg
    return (slope, y_interc)

# setup: import numpy as np ; N = 500000 ; X, Y = np.random.rand(N), np.random.rand(N)
# run: lstsqr(X, Y)
# from:
# http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb

# pythran export lstsqr(float[], float[])
import numpy as np


def lstsqr(x, y):
    """Computes the least-squares solution to a linear matrix equation."""
    x_avg = np.average(x)
    y_avg = np.average(y)
    dx = x - x_avg
    var_x = np.sum(dx**2)
    cov_xy = np.sum(dx * (y - y_avg))
    slope = cov_xy / var_x
    y_interc = y_avg - slope * x_avg
    return (slope, y_interc)
