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import sys
sys.path.append("/path/to/anvill/python")
import anvill

p = anvill.get_program(cache=False)
p.add_function_definition(here())

open("/tmp/spec.json", "w").write(p.proto())

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from multiprocessing import Process

def bigJob():
    #
    result = 0
    #
    for i in range(12345678):
        result += i
    print(result)
 

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"""
docs: https://docs.python.org/2/library/struct.html#struct-format-strings.
This example is just for using struct library instead of int.from_bytes.
"""

import struct

bs1 = b'\xff\xff'
bs2 = struct.unpack('<L', bs1)[0]

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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np


# I think my nural network learn x1^3 + x2^2 + x1^1 + 1...
# x 는 -1~1 까지.

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#!/usr/bin/python3

"""Random Forest Examples. 2020-09-17. Author: Yu JaeIL
This example follows https://towardsdatascience.com/random-forest-in-python-24d0893d51c0
These codes are written and test in python 3.7.7.

References,
decision tree:	https://www.youtube.com/watch?v=n0p0120Gxqk&t=5s
randorm forest:	https://www.youtube.com/watch?v=nZB37IBCiSA
One-Hot-Encode:	https://en.wikipedia.org/wiki/One-hot

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=======
Answer
=======
[[p]] : {z, y, alloc_1}
[[alloc_1]] : set()    
[[q]] : {y}
[[z]] : set()
[[x]] : set()
[[y]] : set()
====

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$ humm
# hum
hm3
// comment
; comment
/* comment */

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async function makeId(number){
	let bitMask1 = 0xffffffffffffffffffffffffffffffff00000000000000000000000000000000;
	let bitMask2 = 0x00000000000000000000000000000000ffffffffffffffffffffffffffffffff;
	let bitArray = new Uint32Array(2);

	bitArray[0] = bitMask1^number;
	bitArray[1] = bitMask2^number;

	let hashBuffer = await crypto.subtle.digest('sha-256',bitArray);
	let hashArray = Array.from(new Uint8Array(hashBuffer));

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#include <stdio.h>
#include <unistd.h>
#include <errno.h>
#include <string.h>
#include <sys/mman.h>

void foo(void);
int change_page_permissions_of_address(void *addr);

int main(void)

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#include <stdio.h>

int main(){
  printf("Hello World.");
  return 0;
}