This changes the calling convention for calling a Javascript function from Python so that any proxied arguments are automatically destroyed at the end of the function call. This is a breaking change. It should make it much more convenient to use the foreign function interface. 

The docs need to be updated to explain the new calling convention before this can be merged. We should put a link to the appropriate section in the docs into the error message emitted when a borrowed proxy is used after it is free so that people have an easy time learning the model by trial and error.
ENH Destroy proxies created in JsProxy.__call__ when done with call. This changes the calling convention for calling a Javascript function from Python so that any proxied arguments are automatically destroyed at the end of the function call. This is a breaking change. It should make it much more convenient to use the foreign function interface. 

The docs need to be updated to explain the new calling convention before this can be merged. We should put a link to the appropriate section in the docs into the error message emitted when a borrowed proxy is used after it is free so that people have an easy time learning the model by trial and error.
# from http://stackoverflow.com/questions/77999777799977/numpy-vs-cython-speed
# pythran export multiple_sum(float[][])
# setup: import numpy as np ; r = np.random.rand(100,100)
# run: multiple_sum(r)
import numpy as np


def multiple_sum(array):

    rows = array.shape[0]
    cols = array.shape[1]

    out = np.zeros((rows, cols))

    for row in range(0, rows):
        out[row, :] = np.sum(array - array[row, :], 0)

    return out

# from http://stackoverflow.com/questions/77999777799977/numpy-vs-cython-speed
# pythran export multiple_sum(float[][])
# setup: import numpy as np ; r = np.random.rand(100,100)
# run: multiple_sum(r)
import numpy as np


def multiple_sum(array):

    rows = array.shape[0]
    cols = array.shape[1]

    out = np.zeros((rows, cols))

    for row in range(0, rows):
        out[row, :] = np.sum(array - array[row, :], 0)

    return out
