Add hereditary option to as_object_map. This automatically apply as_object_map to the values of the map when the values are "plain old objects".

This behavior is slightly naive but I think it will satisfy most people who have requested this feature.


- [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- [x] Add new / update outdated documentation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;lx,ly=(2**6,2**6);u=np.zeros([lx,ly],dtype=np.double);u[lx//2,ly//2]=1000.0;tempU=np.zeros([lx,ly],dtype=np.double)
# run: diffusion(u,tempU,100)

# pythran export diffusion(float [][], float [][], int)


def diffusion(u, tempU, iterNum):
    """
    Apply Numpy matrix for the Forward-Euler Approximation
    """
    mu = 0.1

    for _ in range(iterNum):
        tempU[1:-1, 1:-1] = u[1:-1, 1:-1] + mu * (
            u[2:, 1:-1]
            - 2 * u[1:-1, 1:-1]
            + u[0:-2, 1:-1]
            + u[1:-1, 2:]
            - 2 * u[1:-1, 1:-1]
            + u[1:-1, 0:-2]
        )
        u[:, :] = tempU[:, :]
        tempU[:, :] = 0.0

# setup: import numpy as np;lx,ly=(2**6,2**6);u=np.zeros([lx,ly],dtype=np.double);u[lx//2,ly//2]=1000.0;tempU=np.zeros([lx,ly],dtype=np.double)
# run: diffusion(u,tempU,100)

# pythran export diffusion(float [][], float [][], int)


def diffusion(u, tempU, iterNum):
    """
    Apply Numpy matrix for the Forward-Euler Approximation
    """
    mu = 0.1

    for _ in range(iterNum):
        tempU[1:-1, 1:-1] = u[1:-1, 1:-1] + mu * (
            u[2:, 1:-1]
            - 2 * u[1:-1, 1:-1]
            + u[0:-2, 1:-1]
            + u[1:-1, 2:]
            - 2 * u[1:-1, 1:-1]
            + u[1:-1, 0:-2]
        )
        u[:, :] = tempU[:, :]
        tempU[:, :] = 0.0
