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def plates(stack, i, num_used, N, P, K):
    # Brute force: O(K^N)
    if i >= N:
        return -1
    current_total = -1
    for n in range(K + 1):
        if num_used + n > P:
            break
        elif num_used + n == P:
            current_total = max(current_total, sum(stack[i][:n]))

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All code is highly based on Ildoo Kim's code (https://github.com/ildoonet/tf-openpose) and derived from the OpenPose Library (https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/LICENSE)

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#include <cudnn.h>
#include <iostream>

int main(int argc, char** argv) {
    std::cout << "CUDNN_VERSION: " << CUDNN_VERSION << std::endl;
    return 0;
}

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package main

import (
	"fmt"
	"net/http"
)

type String string

type Struct struct {

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# http://stackoverflow.com/questions/24685436/efficient-way-to-cluster-colors-using-k-nearest

import numpy as np
import cv2

def nearest(i, j, src, knn, colors):
    sample = np.reshape(src, (-1, 3)).astype(np.float32)
    retval, result, neighbors, dist = knn.find_nearest(sample, 1)
    return colors[result[0, 0]]

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#include <cstdio>
#include <string>

int n = 4;
int k = 15;
int a[] = {1, 2, 4, 7};

bool dfs(int i, int sum)
{
  printf("i == %d, sum == %d\n", i, sum);

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#-*- coding: utf-8 -*-

import cv2
from collections import defaultdict


class ImageFlip(object):
    def __init__(self):
        self._observers = []

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#-*- coding: utf-8 -*-


from pit import Pit
import requests
import getpass
import json

HTTPUnauthorized = 401
HTTPCreated = 201

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#include <opencv/cv.h>
#include <opencv/highgui.h>
#include <glut.h>

static void display(void)
{
  cv::Mat img = cv::imread("test.jpg");
  cv::flip(img, img, 0);
  cv::cvtColor(img, img, CV_BGR2RGB);