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def delete_labels(client, label_id_list):
    res = client.execute('''
            mutation DeleteLabels($label_id_list: [ID!]) {
                deleteLabels(
                    labelIds: $label_id_list
                ){
                    id
                    deleted
                }

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.idea/
data/
model/
logdir/

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"""
#TODO change
BigBang-DataScience.com

"""
import random

import tensorflow as tf
import dataUtils

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apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
  name: cluster-autoscaler
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1beta1

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import tensorflow as tf
import numpy as np
import random

def main():

    label_index = {
        'Iris-setosa': 0,
        'Iris-versicolor': 1,
        'Iris-virginica': 2

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float pulsewidth = 10; // 1/100 a second
long count = 0;
long goal = 1024;

void setup() {
  pinMode(13, OUTPUT);
}

void loop() {
  delay(pulsewidth);

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import sys
import uuid
from apache_beam.io import iobase
from apache_beam.transforms import PTransform;
from apache_beam.utils.pipeline_options import PipelineOptions
from apache_beam.io.gcp.datastore.v1.datastoreio import ReadFromDatastore
from apache_beam.io.gcp.datastore.v1.datastoreio import WriteToDatastore
from apache_beam.metrics.metric import MetricsFilter
from apache_beam.utils.pipeline_options import GoogleCloudOptions
from apache_beam.utils.pipeline_options import PipelineOptions

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import MySQLdb
from MySQLdb.cursors import SSCursor
from google.cloud import datastore
from google.cloud.exceptions import TooManyRequests

import json
import time

def chunks(l, n):
    """Yield successive n-sized chunks from l."""

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from apache_beam.metrics.metric import MetricsFilter
from apache_beam.utils.pipeline_options import GoogleCloudOptions
from apache_beam.utils.pipeline_options import PipelineOptions
from apache_beam.utils.pipeline_options import SetupOptions
from apache_beam.utils.pipeline_options import StandardOptions
import googledatastore
import apache_beam as beam

import json
import time

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from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm


def drange(x, y, jump):
    while x < y:
        yield float(x)
        x += jump