neulab/odex huggingface dataset:
# 時系列データの入ったデータフレーム`df`を1分ごとにリサンプルし、間の値は直前の値で補完する
def f_43322(df):
	return df.resample('1min').ffill()

import io
import pandas as pd

def check(candidate):
    data = (
        "年月日時,気温(℃),降水量(mm),風速(m/s),日射量(MJ/㎡)\n"
        "2017-01-01 00:00:00,5.8,0.0,1.5,0.0\n"
        "2017-01-01 01:00:00,4.9,0.0,0.8,0.0\n"
        "2017-01-01 02:00:00,4.9,0.0,1.5,0.0\n"
        "2017-01-01 03:00:00,4.2,0.0,0.8,0.0\n"
        "2017-01-01 04:00:00,4.4,0.0,1.0,0.0\n"
    )
    df = pd.read_csv(io.StringIO(data), parse_dates=['年月日時'], index_col='年月日時')
    res = candidate(df)
    assert len(res) == 241
# 複素数`z`の実数部のみを得る
def f_35683(z):
	return z.real

def check(candidate):
    assert candidate(1.23-0j) == 1.23

    assert candidate(1.23+0j) == 1.23

    assert candidate(0.0-1j) == 0.0
# イテラブルオブジェクト`iter`の`r`個の要素の組み合わせをリストとして得る
def f_41058(iter, r):
	return list(itertools.combinations(iter, r))

import itertools 

def check(candidate):
    assert candidate([1,2,3], 2) == [(1, 2), (1, 3), (2, 3)]

    assert candidate([1], 2) == []

    assert candidate([1], 1) == [(1, )]
# Excelシートオブジェクト`sheet`内の行`row`、列`col`のセルの値を得る
def f_42573(sheet, row, col):
	return sheet.cell_value(row, col)

import xlrd
from xlwt import Workbook

def check(candidate):
    file_location = "test.xlsx"

    book = Workbook()
    sheet1 = book.add_sheet('Sheet 1')
    sheet1.write(0, 0, 'A1')
    sheet1.write(0, 1, 'B1')
    sheet1.write(8, 5, "Hello, world!")
    book.save(file_location)

    workbook = xlrd.open_workbook(file_location)
    sheet = workbook.sheet_by_index(0)
    assert candidate(sheet, 0, 0) == "A1"
    assert candidate(sheet, 0, 1) == "B1"
    assert candidate(sheet, 8, 5) == "Hello, world!"
# 引数`args`をアンパックして関数`func`に渡す
def f_40361(func, args):
	return func(*args)

def check(candidate):
    def func1(x, y, z): return x + y + z 
    assert candidate(func1, [1,2,3]) == 6

    def func2(a): return 0.8
    assert candidate(func2, ['random']) == 0.8
# 列名`range`の要素をリスト`r`、列名`result`の要素をリスト`l`としてデータフレームを作る
def f_43333(r, l):
	return pd.DataFrame(data={'range': r, 'result': l})

import pandas as pd

def check(candidate):
    r, l = [1,2,3], [4,5,6]
    assert candidate(r, l).equals(pd.DataFrame(data={'range': r, 'result': l}))
# ファイル`C:\Users\Documents\python programs`を開く
def f_11582():
	return open('C:\\Users\\Documents\\python programs', 'r', encoding='utf-8')

import builtins
from unittest.mock import Mock

def check(candidate):
    with open('a.txt', 'w') as f:
        f.write('t')
    f1 = open('a.txt')
    builtins.open = Mock(return_value = f1)
    assert candidate() == f1
# PYTHONPATHを表示する
def f_12174():
	return sys.path

import sys

def check(candidate):
    assert candidate() == sys.path
# テキストファイル`myfile.txt`の行数を取得する
def f_6225():
	return sum(1 for line in open('myfile.txt'))

def check(candidate):
    with open('myfile.txt', 'w') as fw:
        for i in range(10): fw.write(f"{i}\n")
    assert candidate() == 10

    with open('myfile.txt', 'w') as fw:
        for i in range(88): fw.write(f"{i}\n")
    assert candidate() == 88
# テキストファイル`myfile.txt`の行数を取得する
def f_6225():
	return len(open('myfile.txt').readlines())

def check(candidate):
    with open('myfile.txt', 'w') as fw:
        for i in range(10): fw.write(f"{i}\n")
    assert candidate() == 10

    with open('myfile.txt', 'w') as fw:
        for i in range(88): fw.write(f"{i}\n")
    assert candidate() == 88
# 変数`a`がNoneでない場合に変数を表示する
def f_47199(a):
	return a is not None

def check(candidate):
    assert candidate(None) == False

    assert candidate(0) == True

    assert candidate(0.00) == True

    assert candidate([]) == True

    assert candidate(102) == True
# 二次元リスト`list`の中身を全て
def f_23332(data):
	
	list = []
	for r in data:
		list.append(', '.join(r))

	return list

def check(candidate):
    data = [['a','b','c','x','y','z'],
            ['f', 'g', 'h', 'i', 'j', 'k']]
    assert candidate(data) == ["a, b, c, x, y, z", "f, g, h, i, j, k"]
# 文字コード
def f_34431():
	f = open('all_names.csv', 'w', encoding='UTF-8') 
	return f

def check(candidate):
    f = candidate()
    assert f.name == 'all_names.csv'
    assert f.mode == 'w'
    assert f.encoding == 'UTF-8'
# リスト`list`から条件となる文字列`string`と部分一致する要素を取り出して新しいリスト`newlist`を作る
def f_33700(list):
	
	newlist = []
	for s in list: 
		if s.endswith('string'): 
			newlist.append(s)

	return newlist

def check(candidate):
    assert candidate(['abcstring', 'bbbb', 'fhstringyjn', '1326546']) == ['abcstring']
# リスト`list`から条件となる文字列`string`と部分一致する要素を取り出して新しいリスト`newlist`を作る
def f_33700(list):
	newlist = []
	for s in list:
	    if 'string' in s:
	        newlist.append(s)
	return newlist

def check(candidate):
    assert candidate(['abcstring', 'bbbb', 'fhstringyjn', '1326546']) == ['abcstring', 'fhstringyjn']
# グラフを表示する
def f_27556():
	return plt.figure() 

import matplotlib
import matplotlib.pyplot as plt

def check(candidate):
    assert isinstance(candidate(), matplotlib.figure.Figure)
# データフレーム`data_frame`を表示する
def f_37060(data_frame):
	return display(data_frame)

import pandas as pd
from IPython.display import display

def check(candidate):
    df = pd.DataFrame([1, 2, 3])
    try:
        candidate(df)
    except:
        assert False
# 表`table`を画面に表示する（texttable)
def f_12310():
	table = Texttable()
	print(table.draw())
	return table

from texttable import Texttable

def check(candidate):
    try:
        candidate()
    except:
        assert False
# 少数点にコンマが使われているファイル`arena.txt`を読み込む
def f_19311():
	return pd.read_csv('arena.txt', header=None, delim_whitespace=True, decimal=',')

import pandas as pd

def check(candidate):
    file_name = 'arena.txt'
    with open(file_name, 'w') as f:
        f.write('1   0,000000    4,219309    4,219309    8,988674    8,988674    10,848450\n')
        f.write('2   4,219309    7,414822    7,414822    12,430150   12,430150   14,198310\n')
        f.write('3   8,000000    10,478795   10,478795   15,417747   15,417747   17,297929\n')
        f.write('1   11,000000   14,257995   14,257995   19,009302   19,009302   20,873072\n')
    df = candidate()
    assert df.shape[0] == 4
    assert df.shape[1] == 7
# オブジェクト`json_data`をJSON文字列`json_str`に変換する
def f_42268(json_data):
	json_str = json.dumps(json_data)
	return json_str

import json

def check(candidate):
    assert candidate({'a': 134, 'bvgdfbh': 46.7576}) == '{"a": 134, "bvgdfbh": 46.7576}'

    assert candidate(['foo', {'bar': ('baz', None, 1.0, 2)}]) == '["foo", {"bar": ["baz", null, 1.0, 2]}]'
# CSVファイル`file.csv`を`data`に読み込む
def f_51387():
	data = np.genfromtxt('file.csv')
	return data

import numpy as np
from unittest.mock import Mock

def check(candidate):
    np.genfromtxt = Mock(return_value = np.array([[2, 3, 5], [1, 5, 6]]))
    assert candidate().shape == (2, 3)

    np.genfromtxt = Mock(return_value = np.array([['abc'], ['lkm']]))
    assert candidate().shape == (2, 1)
# 条件`condition`を満たす行を削除する
def f_38598(df):
	df.drop(df.index[df.row == "condition"], inplace=True)
	return 

import pandas as pd

def check(candidate):
    df = pd.DataFrame({'row': ['同意します', 'condition', '同意します', '同意しません',]},
    index=[1, 2, 3, 4, ])
    candidate(df)
    assert df.shape == (3, 1)
# 二次元リスト`list`内の要素
def f_20094(list, x, y):
	return list[x][y]

import json

def check(candidate):
    mat = [[i+j for i in range(2)] for j in range(3)]
    assert candidate(mat, 0, 0) == 0

    mat = [[i+j for i in range(2)] for j in range(3)]
    assert candidate(mat, 2, 1) == 3
# forループで数字を添字に持つ二次元リスト`list`を生成する
def f_18780(x, y):
	for i in range(x):
	  for j in range(y):
	    exec("list_" + str(i) + "_" + str(j) + "= [i, j]")
	return 

def check(candidate):
    try:
        candidate(3, 4)
    except:
        assert False
# ファイル名に現在の日付を入れる
def f_19552():
	now = datetime.datetime.now()
	file_name = 'file_{0:%Y%m%d-%H%M%S}.txt'.format(now)
	return file_name

import datetime

def check(candidate):
    file_name = candidate()
    later_name = 'file_{0:%Y%m%d-%H%M%S}.txt'.format(datetime.datetime.now())
    assert file_name.split('-')[0] == later_name.split('-')[0]

    file_time = int(file_name.split('-')[1].split('.')[0])
    later_time = int(later_name.split('-')[1].split('.')[0])
    assert (later_time - file_time) < 100
# グラフに線形回帰直線を追加する
def f_38755(x, y):
	
	a, b = np.polyfit(x, y, 1)
	linear = a * x + b
	img = plt.plot(x, linear,color="black")

	return img

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

def check(candidate):
    x = np.linspace(0,1,100)
    y = np.random.rand(100)
    try:
        img = candidate(x, y)
        assert type(img[0]) == matplotlib.lines.Line2D
    except:
        assert False
# メンバ関数からクラスの名前を取得する
def f_4556():
	class Foo:
	    def whoAmI(self):
	        print( "I am " + self.__class__.__name__)
	
	Foo().whoAmI()
	return 

import sys

def check(candidate):
    f = open('output', 'w')
    sys.stdout = f
    candidate()
    f.close()
    with open ('output', 'r') as f1:
        lines = f1.readlines()
        assert 'I am Foo' in lines[0]
# ユーザーエージェントをFirefoxに変更する
def f_27922():
	return User-Agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:47.0) Gecko/20100101 Firefox/47.0

import urllib.request

def check(candidate):
    url = "https://en.wikipedia.org/wiki/List_of_national_independence_days"
    request = urllib.request.Request(url=url, headers=candidate())
    response = urllib.request.urlopen(request)
    assert response.getcode() == 200
# 空白で区切られたCSVファイル`file`を読み込む
def f_35394(file):
	return pd.read_csv(file, sep='\s+')

import pandas as pd

def check(candidate):
    file_name = 'a.csv'
    with open (file_name, 'w') as f:
        f.write('1 2\n')
    df = candidate(file_name)
    assert df.shape == (0, 2)

    with open (file_name, 'w') as f:
        f.write('abc def\nefg hij')
    df = candidate(file_name)
    assert df.shape == (1, 2)
# 空白で区切られたCSVファイル`file`を読み込む
def f_35394(file):
	return pd.read_csv(file, delim_whitespace=True)

import pandas as pd

def check(candidate):
    file_name = 'a.csv'
    with open (file_name, 'w') as f:
        f.write('1 2\n')
    df = candidate(file_name)
    assert df.shape == (0, 2)

    with open (file_name, 'w') as f:
        f.write('abc def\nefg hij')
    df = candidate(file_name)
    assert df.shape == (1, 2)
# 変数`variable`に値が入っていない場合のみ値を代入をする
def f_37591(variable, value):
	variable = value if variable is None else variable
	return variable

def check(candidate):
    assert candidate(123, 1) == 123

    assert candidate(None, 1) == 1

    assert candidate([], 1) == []
# Windows上のアプリケーション`app`を実行する
def f_11601():
	os.startfile('C:\Program Files\....\app.exe')
	return 

import os
from unittest.mock import Mock

def check(candidate):
    os.startfile = Mock()
    try:
        candidate()
    except:
        assert False
# 数値`number`を一桁ずつ取得してリスト`num_list`にする
def f_26837(number):
	
	num_list = []
	while number != 0:
		num_list.append(number % 10)
		number //= 10
	num_list.reverse()

	return num_list

def check(candidate):
    assert candidate(123) == [1,2,3]
# 数値`number`を一桁ずつ取得してリスト`num_list`にする
def f_26837(number):
	num_list = map(int, str(number))
	return num_list

def check(candidate):
    assert list(candidate(123)) == [1,2,3]
# 定数の評価結果を表示する
def f_59780():
	
	def example(a, b): return b
	hello  = tf.constant("Hello")
	f = tf.function(example)
	result = eval(f([], hello))

	return result

import tensorflow as tf
from tensorflow.keras.backend import eval

def check(candidate):
    assert candidate() == b'Hello'
# 正規表現で文字列`text`の中から数値だけを抽出してリスト`list`にする
def f_38276(text):
	
	pattern = r"([0-9]+)"
	list=re.findall(pattern,text)

	return list

import re

def check(candidate):
    assert candidate('fg456fgxnd') == ['456']
# y軸の下限値を指定し、上限値は自動にする
def f_49558(x, y, df):
	rp = sns.regplot(x, y, data=df, order=1, line_kws={"color":"indianred"})
	rp.axes.set_ylim(0,)
	return rp

import pandas as pd
import seaborn as sns

def check(candidate):
    df = pd.DataFrame([[0, 1, 2], [7, 8, 9]])
    rp = candidate(df[0], df[1], df)
    assert 'Axes' in str(type(rp))
# 文字列`word`の順番を`h`番目で入れ替える
def f_65284(word, h):
	tmp1 = word[:h]
	word = word[h:]
	word.extend(tmp1)
	return word

def check(candidate):
    assert candidate(["a","b","c","d","e","f","g"], 4) == ["e","f","g","a","b","c","d"]
# リスト`attributes`の全要素の数値に対してfor文でマイナス1する
def f_31916(attributes):
	for i, attribute in enumerate(attributes):
	    attributes[i] = attribute-1
	return attributes

def check(candidate):
    assert candidate([1,2,3]) == [0,1,2]

    assert candidate([100]) == [99]
# リスト`attributes`の全要素の数値に対してfor文でマイナス1する
def f_31916(attributes):
	for i in range(len(attributes)):
	    attributes[i] = attributes[i]-1
	return attributes

def check(candidate):
    assert candidate([1,2,3,4]) == [0,1,2,3]

    assert candidate([1., 2, 3.4, 5.5]) == [0., 1, 2.4, 4.5]
# リスト`attributes`の全要素の数値に対してfor文でマイナス1する
def f_31916(attributes):
	attributes = [attribute-1 for attribute in attributes]
	return attributes

def check(candidate):
    assert candidate([1,2,3,4]) == [0,1,2,3]

    assert candidate([1., 2, 3.4, 5.5]) == [0., 1, 2.4, 4.5]
# 二次元リスト`list`から重複する要素のみ抽出する
def f_21171(list):
	return Counter(map(tuple, list))

from collections import Counter

def check(candidate):
    li=[[1,2,3],[2,3,4],[3,4,5],[4,5,6],[2,3,4],[1,2,3],[2,3,4],[5,6,7]]
    c = candidate(li)
    assert c.most_common() == [((2, 3, 4), 3), ((1, 2, 3), 2), ((3, 4, 5), 1), ((4, 5, 6), 1), ((5, 6, 7), 1)]

    li = [['abc', 'def'], ['hij', 'klm']]
    c = candidate(li)
    assert c.most_common() ==[(('abc', 'def'), 1), (('hij', 'klm'), 1)]
# 連番個数`num_of_file`のtxtファイル`data{}.txt`を読み込む
def f_45204(num_of_file):
	data = [None] * num_of_file
	for i in range(num_of_file):
	    with open('data{}.txt'.format(i + 1), mode="r", encoding="utf-8") as f:
	        data[i] = f.read()
	return data

def check(candidate):
    num_of_file = 4
    for i in range(0, num_of_file):
        with open ('data'+str(i + 1)+'.txt', 'w') as f:
            f.write(str(i + 1)+'\n')
    data = candidate(4)
    for i in range(0, num_of_file):
        assert data[i] == str(i + 1)+'\n'
# 文字列`string`をbyte型
def f_24786(string):
	return urllib.parse.urlencode(string).encode('ascii')

import urllib

def check(candidate):
    s = {'mail':'admin@getgo.com', 'password':34}
    assert candidate(s) == b'mail=admin%40getgo.com&password=34'
# 主成分分析 
def f_5822(imgAry):
	pca = PCA()
	pca.fit(imgAry)
	pca_res = pca.transform(imgAry)
	restoredImgAry = pca.inverse_transform(pca_res)
	return restoredImgAry

import numpy as np 
from sklearn.decomposition import PCA

def check(candidate):
    imgAry = np.array([[1, 2], [4, 3]])
    assert np.allclose(candidate(imgAry), np.array([[1,2],[4,3]], dtype=float))
# グラフサイズを調整して保存する
def f_42516(fnameF):
	return plt.savefig(fnameF, dpi=200, bbox_inches="tight", pad_inches=0.1)

import os
import matplotlib.pyplot as plt

def check(candidate):
    candidate('v.jpg')
    assert os.path.exists('v.jpg')
# 二次元リスト`list`から空白の要素を削除する
def f_18685(list):
	list = [x for x in list if x]
	return list

def check(candidate):
    assert candidate(['afrg ', 'fdbf', 13254, 54765.6]) == ['afrg ', 'fdbf', 13254, 54765.6]

    assert candidate(['', None, 0]) == []
# 二次元リスト`list`から空白の要素を削除する
def f_18685(list):
	for i in range(len(list) - 1, -1, -1):
	  if not list[i]:
	    del list[i]
	return list

def check(candidate):
    assert candidate([4,3,2,0,1,0]) == [4,3,2,1]

    assert candidate([4,3,2,[],1,0]) == [4,3,2,1]
# JSON文字列`json_string`をオブジェクト`json_obj`に読み込む（json)
def f_34692(json_string):
	json_obj = json.loads(json_string)
	return json_obj

import json

def check(candidate):
    assert candidate('{"a": 5}') == {'a': 5}
# カラー画像`img`をグレースケールで表示する
def f_35864(img):
	gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
	plt.imshow(gray_img)
	plt.gray()
	plt.show()
	return gray_img

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

def check(candidate):
    im = Image.new('RGBA', (200, 200), (255, 255, 255, 255))
    im.save('v.png')
    img = cv2.imread('v.png')
    gray_img = candidate(img)
    cv2.imwrite('v_gray.png', gray_img)
    assert os.path.exists('v_gray.png')
# X軸の間隔を`ticks`に、ラベルを`labels`にする
def f_49478(ticks, labels):
	return plt.xticks(range(0, len(labels), ticks), labels[::ticks])

import matplotlib.pyplot as plt

def check(candidate):
    ticks = 3
    labels = [1, 4]
    plt = candidate(ticks, labels)
    assert isinstance(plt, tuple)

    labels = ['abc', 'xcv']
    plt = candidate(ticks, labels)
    assert isinstance(plt, tuple)
# 連番になっている`num_of_file`個のExcelファイル`excel_file`をリストに読み込む
def f_16769(num_of_file):
	book_list = []
	for n in range(1, num_of_file + 1):
	    file_name = 'excel_file%d.xls' % (n)
	    book_list.append(xlrd.open_workbook(file_name))
	return book_list

import xlrd
import xlwt

def check(candidate):
    num_files = 3
    for i in range(0, num_files):
        workbook = xlwt.Workbook()
        sheet = workbook.add_sheet('test')
        sheet.write(0, 1, 1)

        workbook.save('excel_file%d.xls' % (i + 1))
    book_list = candidate(num_files)
    for bk in book_list:
        assert isinstance(bk, xlrd.book.Book)
# datetimeオブジェクト`dt`からUnix Timeを求める
def f_2220(dt):
	return dt.timestamp()

import pytz
import datetime

def check(candidate):
    dt = datetime.datetime.fromtimestamp(123456789.123456, pytz.timezone('America/Los_Angeles'))
    assert candidate(dt) == 123456789.123456
# 行`col_name`に特定の文字列`string`を含む行をデータフレーム`df`から抽出する
def f_44723(df, col_name, string):
	return df[df[col_name].str.contains(string)]

import pandas as pd 

def check(candidate):
    df = pd.DataFrame({'name': ['Mr. A', 'Ms. B'], 'age': [30, 23]})
    df_sub = pd.DataFrame({'name': ['Mr. A'], 'age': [30]})
    assert candidate(df, 'name', 'A').equals(df_sub)
# リスト`list`の末尾から`n`個の要素を取り出す
def f_46711(list, n):
	return list[-n:]

def check(candidate):
    assert candidate([1,2,3,4,5,6,7], 2) == [6,7]

    assert candidate([1,2,3], 0) == [1,2,3]
# Unicodeエスケープされたバイト列`b_string`を文字列に変換
def f_17648(b_string):
	return b_string.decode('unicode-escape')

import unicodedata

def check(candidate):
    assert candidate(b'example-string') == 'example-string'
# 文字列`str`内の対象文字列`old_s`を別の文字列`new_s`に置換する
def f_9633(str, old_s, new_s):
	return str.replace(old_s, new_s)

def check(candidate):
    assert candidate('mystring', 'my', 'your') == 'yourstring'
# 空行が入力されるまで標準入力を受け付ける
def f_37327():
	
	while True:
		try:
			line = input()
			if line == '':
				break
			else:
				yield line
		except EOFError:
			break

	return 

import sys

def check(candidate):
    with open('p.txt', 'w') as f:
        f.write('1\n\n')
    f = open('p.txt')
    sys.stdin = f
    d = candidate()
    print(type(d))
    assert 'generator' in str(type(d))
    sys.stdin = sys.__stdin__
# ログを出力せずにモデルの学習を行う
def f_37831(model, X, Y):
	model.fit(X, Y, epochs=200, batch_size=1, verbose=0)
	return 

import numpy as np
from tensorflow.keras import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *

def check(candidate):
    model = Sequential([
        Dense(input_dim=2, units=1), Activation('sigmoid')
    ])
    model.compile(loss='binary_crossentropy', optimizer=SGD(lr=0.1))
    X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
    Y = np.array([[0], [1], [1], [1]])

    try:
        candidate(model, X, Y)
    except:
        assert False
# 文字列`str`をバイト列
def f_53340(str):
	return str.encode('utf-8')

def check(candidate):
    assert candidate('mystr') == b'mystr'
# 要素がデータフレームで、要素数`n`のリストを作る
def f_45120(n):
	return [pd.DataFrame() for i in range(n)]

import pandas as pd 

def check(candidate):
    df_list = candidate(3)
    assert len(df_list) == 3
    assert all([type(a)==pd.DataFrame for a in df_list])
# 文字列`str`の中の連続した複数の全角空白を1つの全角空白で置換する
def f_24987(str):
	return re.sub('[\u3000]{2,}', '\u3000', str)

import re

def check(candidate):
    text = u"名前１　　　名前２　　　名前３　名前４　　　"
    assert candidate(text) == '名前１　名前２　名前３　名前４　'
# 画像`img`をファイル名`file.jpg`として保存する
def f_50500(img):
	return cv2.imwrite('file.jpg', img)

import cv2
from os import path
from PIL import Image

def check(candidate):
    im = Image.new('RGBA', (200, 200), (255, 255, 255, 255))
    im.save('v.png')
    img = cv2.imread('v.png')
    candidate(img)
    assert path.exists('file.jpg')
# 行列`a`と`b`の積を計算する
def f_41336(a, b):
	return np.dot(a, b)

import numpy as np

def check(candidate):
    arr = np.array([[1,3,-5]])
    arr_1 = np.array([[4],[-2],[-1]])
    assert candidate(np.mat(arr),np.mat(arr_1)) == 3

    assert np.array_equal(candidate(np.zeros(shape=(5,2)), np.zeros(shape=(2,5))), np.zeros(shape=(5,5)))

    assert np.array_equal(candidate(np.array([[4]]), np.array([[0]])), np.array([[0]]))

    assert candidate(3,4) == 12
# 空白区切りのテキストファイル`file.txt`をコンマ区切りのcsvファイル`new_file.csv`に変換する
def f_48742():
	df = pd.read_table('file.txt', header=None, delim_whitespace=True)
	df.to_csv('new_file.csv', index=False, header=False)
	return 

import csv
import pandas as pd

def check(candidate):
    with open("file.txt", "w") as text_file:
        text_file.write('''col1 col2 col3
''')
        text_file.write('''1 2 3
''')
    
    candidate()
    import csv
    file = open('new_file.csv')
    csvreader = csv.reader(file)
    rows = []
    for row in csvreader:
            rows.append(row)
    
    assert rows[0] == ['col1', 'col2', 'col3'] and rows[1] == ['1', '2', '3']

    with open("file.txt", "w") as text_file:
        text_file.write('''col1
''')
        text_file.write('''1
''')
        text_file.write('''2
''')
    
    candidate()
    import csv
    file = open('new_file.csv')
    csvreader = csv.reader(file)
    rows = []
    for row in csvreader:
            rows.append(row)
    
    assert rows[0] == ['col1'] and rows[1] == ['1'] and rows[2] == ['2']

    with open("file.txt", "w") as text_file:
        text_file.write('''col1    col2   col3  
''')
        text_file.write('''  1   2  3 
''')
    
    candidate()
    import csv
    file = open('new_file.csv')
    csvreader = csv.reader(file)
    rows = []
    for row in csvreader:
            rows.append(row)
    
    assert rows[0] == ['col1', 'col2', 'col3'] and rows[1] == ['1', '2', '3']
# 数値`n`を文字列に変換する
def f_35271(n):
	return str(n)

def check(candidate):
    assert candidate(0) == '0'

    assert candidate(34.12) == '34.12'

    assert candidate(-1) == '-1'

    assert candidate(float('inf')) == 'inf'

    assert candidate(123412) == '123412'
# 2次元リスト`li`内の重複している要素を取り出す
def f_20048(li):
	
	c = Counter(tuple(x) for x in li)
	result = [list(k) for k,v in c.items() if v >=2]

	return result

from collections import Counter

def check(candidate):
    li=[[1,2,3],[5,6,7],[2,3,4,5],[1,2,3],[7,8,9],[2,3,4,5],[1,2,3],[5,6,7]]
    assert candidate(li) == [[1, 2, 3], [5, 6, 7], [2, 3, 4, 5]]
# 2つのリスト`li`と`li2`を比較し、重複している要素を削除する
def f_21070(li, li2):
	return list(filter(lambda x:x not in li2, li))

def check(candidate):
    assert candidate([1, 2, 3], [1, 3, 4]) == [2]

    assert candidate(['abc', 'def'], ['abc']) == ['def']
# 整数を要素に持つリスト`li'から、合計すると0になる3つの整数を求める
def f_19098(li):
	
	result=[]
	li.sort()
	M = len(li) - 1
	for i, e in enumerate(li):
		j = i + 1
		k = M
		while k > j:
			s = li[i] + li[j] + li[k]
			if s == 0:
				result.append([li[i], li[j], li[k]])
				k -= 1
			elif s > 0:
				k -= 1
			else:
				j += 1

	return result

def check(candidate):
    li = [1, -1, 0, 2]
    assert len(candidate(li)) == 1
# 配列`ary`の各要素にコンマを付けて小数点四桁まで表示する
def f_36377(ary):
	return np.array2string(ary, separator=', ', formatter={'float_kind': lambda x: '{: .4f}'.format(x)})

import numpy as np

def check(candidate):
    assert candidate(np.array([1, 2, 3])) == "[1, 2, 3]"

    assert candidate(np.array([])) == "[]"

    assert candidate(np.array([1, 2, 3, 4, 4])) == "[1, 2, 3, 4, 4]"

    assert candidate(np.array([1, 2, 3, 4, 5])) != "[1 2 3 4 5]"
# 外部プロセス`cat`を呼び出し、ファイル`file`の中身を読み込む
def f_23839():
	return subprocess.check_output('cat file', shell=True)

import subprocess
from unittest.mock import Mock

def check(candidate):
    subprocess.check_output = Mock(return_value = "Success")
    assert candidate() == "Success"
# 画像'file.png'をグレースケールで読み込む
def f_34981():
	return cv2.imread('file.png', 0)

import cv2
from os import path
from PIL import Image

def check(candidate):
    im = Image.new('RGBA', (200, 200), (255, 255, 255, 255))
    im.save('file.png')
    img = candidate()
    cv2.imwrite('g.png', img)
    assert path.exists('g.png')