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from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import tensorflow as tf
import horovod.tensorflow as hvd

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// Code from http://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/index.html#onedevprothrd

#define _BSD_SOURCE

#include <stdio.h>
#include "cuda_runtime.h"
#include "nccl.h"
#include "mpi.h"
#include <stdint.h>
#include <stdlib.h>

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import requests
from io import BytesIO
from PIL import Image
import numpy as np
import timeit
import torch

from maskrcnn_benchmark.config import cfg
from predictor import COCODemo, to_image_list

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from __future__ import print_function

import argparse
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data.distributed
from torchvision import datasets, transforms, models
import horovod.torch as hvd
import tensorboardX

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import numba
import numba.cuda
import numpy as np


@numba.jitclass([
    ("x", numba.types.int32)
])
class XYZ:
    def __init__(self, x):

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import graphviz
import numpy as np
import keras


def gather_layer_stats(layer_dict, layer, r, s):
    lr, ls = None, None
    if hasattr(layer, 'kernel_size'):
        assert layer.kernel_size[0] == layer.kernel_size[1]
        assert layer.strides[0] == layer.strides[1]

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from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform, conditional
import keras
import tensorflow as tf
import horovod.keras as hvd
import keras.backend as K
import math

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from __future__ import print_function

import os
import sys
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.python.framework import graph_io

if len(sys.argv) < 2:
    print('Usage: %s <filename prefix>' % sys.argv[0])

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from __future__ import print_function

import argparse
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data.distributed
from torchvision import models
import horovod.torch as hvd
import timeit