worldmind

import numpy as np
import pandas as pd
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import roc_auc_score, precision_score, recall_score, f1_score, accuracy_score
import torch
from torch import nn, tensor, optim
from skorch import NeuralNetClassifier


def get_data():

worldmind

from time import sleep
import signal
import multiprocessing as mp


_SIGTERM = None


def sigterm_handler(signum, frame):
    global _SIGTERM

worldmind

from decimal import Decimal
from fractions import Fraction


def muller_recurrence(y, z):
 return 108 - ((815-1500/z)/y)


N = 25

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from urllib.parse import quote, urlencode, ParseResult as Url


Url.__str__ = Url.geturl

bucket_name = 'something_with space'
file_name = 'data.txt'

url = Url(
    scheme='https',

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from array import array
from pympler.asizeof import asizeof

SIZE = 1000


the_array = array('i')
the_list = []

for_tuple = []