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import io
import os
import numpy as np
from PIL import Image
from pymongo import MongoClient
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms


def pil_loader(f):

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import torch as th                                                                 
                                                                                   
                                                                                   
class NLL_OHEM(th.nn.NLLLoss):                                                     
    """ Online hard example mining. 
    Needs input from nn.LogSotmax() """                                             
                                                                                   
    def __init__(self, ratio):      
        super(NLL_OHEM, self).__init__(None, True)                                 
        self.ratio = ratio                                                         

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git show-branch \
| sed "s/].*//" \
| grep "\*" \
| grep -v "$(git rev-parse --abbrev-ref HEAD)" \
| head -n1 \
| sed "s/^.*\[//"

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/*
Linked_list implementation for cracking the code
questions
*/
#include <iostream>
#include <stdlib.h>	/* srand, rand */
#include <map>

using namespace std;
template <typename dataType> 

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#include <vector>
#include <iostream>
using namespace std;

int main(){
	// This initialization is available at C++11 support compiler
	// For lower version C++ support check the reference document
	vector<int> arr (9);
	arr = {1,2,3,4,5,6,7,8,9};
	

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#include "caffeclassifier.h"

CaffeClassifier::CaffeClassifier(const string& model_file,
                       const string& trained_file,
                       const string& mean_file,
                       const string& label_file,
                       const bool use_GPU,
                       const int batch_size) {
   if (use_GPU)
       Caffe::set_mode(Caffe::GPU);

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function[binary_codes,G_vecs] = WTA_kernel_binary(X,G_vecs,G, K)
% G is number of permutation vectors
% K is the insterest span

if size(G_vecs,1) == 0
        G_vec = zeros(G,size(X,2));
        for i = 1:G 
                G_vecs(i,:) = randperm(size(X,2),size(X,2));
        end
end