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import argparse
import os
import pickle

from keras.datasets import cifar10
from autokeras.image.image_supervised import ImageClassifier, PortableImageSupervised
import autokeras.constant
import torch

from sklearn.metrics import accuracy_score

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#0  0x00007fffe6a70bb8 in svm::Solver::Solve(int, svm::QMatrix const&, double const*, signed char const*, double*, double const*, double, svm::Solver::SolutionInfo*, int, int) ()
   from /home/yusuke/miniconda3/envs/py37_automl_examples2/lib/python3.7/site-packages/sklearn/svm/libsvm.cpython-37m-x86_64-linux-gnu.so
#1  0x00007fffe6a7271b in svm::svm_train_one(svm_problem const*, svm_parameter const*, double, double, int*) ()
   from /home/yusuke/miniconda3/envs/py37_automl_examples2/lib/python3.7/site-packages/sklearn/svm/libsvm.cpython-37m-x86_64-linux-gnu.so
#2  0x00007fffe6a74207 in svm_train ()
   from /home/yusuke/miniconda3/envs/py37_automl_examples2/lib/python3.7/site-packages/sklearn/svm/libsvm.cpython-37m-x86_64-linux-gnu.so
#3  0x00007fffe6a579c9 in __pyx_pf_7sklearn_3svm_6libsvm_fit.isra.21 ()
   from /home/yusuke/miniconda3/envs/py37_automl_examples2/lib/python3.7/site-packages/sklearn/svm/libsvm.cpython-37m-x86_64-linux-gnu.so
#4  0x00007fffe6a5f471 in __pyx_pw_7sklearn_3svm_6libsvm_1fit ()
   fr

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/**
* (C) Copyright IBM Corp. 2015, 2020.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*      http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software

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import gi
gi.require_version('Gtk', '3.0')
from gi.repository import Gdk
from gi.repository import GdkPixbuf
import numpy

def get_window_screen(window_id):
    window = Gdk.get_default_root_window()
    screen = window.get_screen()
    typ = window.get_type_hint()

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import gi
gi.require_version('Gtk', '3.0')
from gi.repository import Gdk
from gi.repository import GdkPixbuf
import numpy

def get_window_screen(window_id):
    window = Gdk.get_default_root_window()
    screen = window.get_screen()
    typ = window.get_type_hint()

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import gi
gi.require_version('Gtk', '3.0')
from gi.repository import Gdk
from gi.repository import GdkPixbuf
import numpy

def get_window_screen(window_id):
    window = Gdk.get_default_root_window()
    screen = window.get_screen()
    typ = window.get_type_hint()

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using UnityEngine;
using VRM;

using AsyncIO;
using NetMQ;
using NetMQ.Sockets;
using MessagePack;

using System;

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import zmq
import sys

context = zmq.Context()
subscriber = context.socket(zmq.SUB)
subscriber.connect("tcp://localhost:5555")

# receive only message with zipcode being 10001
zipfilter = sys.argv if len(sys.argv) > 1 else "Detection"
subscriber.setsockopt(zmq.SUBSCRIBE, zipfilter.encode('utf-8'))

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# MediaPipe graph that performs multi-hand tracking with TensorFlow Lite on GPU.
# Used in the examples in
# mediapipe/examples/android/src/java/com/mediapipe/apps/multihandtrackinggpu.

# Images coming into and out of the graph.
input_stream: "input_video"
output_stream: "output_video"

# Collection of detected/processed faces, each represented as a list of
# landmarks. (std::vector<NormalizedLandmarkList>)

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