code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
@register.inclusion_tag('user/user_list.html')
def user_list():
"""show user name list"""
users = User.objects.all()
return {'users': users}
@register.simple_tag()
def accept_request(pk_login_user, pk_other_user):
RequestFollow.objects.accept_request(pk_login_user, pk_ot... | flexible | {
"blob_id": "999c19fd760ffc482a15f5a14e188d416fcc5f21",
"index": 7218,
"step-1": "<mask token>\n\n\n@register.inclusion_tag('user/user_list.html')\ndef user_list():\n \"\"\"show user name list\"\"\"\n users = User.objects.all()\n return {'users': users}\n\n\n@register.simple_tag()\ndef accept_request(pk... | [
4,
5,
6,
7,
8
] |
import sys
import pathlib
from matplotlib import pyplot as plt
import matplotlib as mpl
script_name = pathlib.Path(sys.argv[0]).stem
FIGURES_DIR = pathlib.Path(
__file__).parents[2] / "figures" / "simulations" / script_name
FIGURES_DIR.mkdir(exist_ok=True, parents=True)
# mpl.rc("text", usetex=True)
# mpl.rc("fo... | normal | {
"blob_id": "fc26574ac8628d7e2896e3e6d055ac61264c7db0",
"index": 1302,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nFIGURES_DIR.mkdir(exist_ok=True, parents=True)\n",
"step-3": "<mask token>\nscript_name = pathlib.Path(sys.argv[0]).stem\nFIGURES_DIR = pathlib.Path(__file__).parents[2\n ] / 'figure... | [
0,
1,
2,
3,
4
] |
# Class 1: Flight which contains the flight number(f_id), its origin and destination, the number of stops between the
# origin and destination and the type of airlines(f_type)
class Flight():
# INIT CONSTRUCTOR
def __init__(self, f_id, f_origin, f_destination, no_of_stops, flight_type, p_id, p_type):
s... | normal | {
"blob_id": "95a2f5abb37642651316a8954a4289e5b04e4916",
"index": 4357,
"step-1": "<mask token>\n\n\nclass Passenger(Person):\n <mask token>\n <mask token>\n\n def __init__(self, p_id, p_type, p_gender, p_name, p_phonenumber, f_id,\n pno, f_origin, f_destination, no_of_stops, flight_type):\n ... | [
5,
7,
13,
16,
17
] |
<|reserved_special_token_0|>
class BookRoomThread(threading.Thread):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_spe... | flexible | {
"blob_id": "ae775e25179546156485e15d05491e010cf5daca",
"index": 9360,
"step-1": "<mask token>\n\n\nclass BookRoomThread(threading.Thread):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask ... | [
3,
8,
15,
17,
18
] |
<|reserved_special_token_0|>
class Solution:
def __new__(self, p):
nr_counts, nr_consonants, replaced = self.count_vowels_consonants(self,
p)
inversed = ''.join(c.lower() if c.isupper() else c.upper() for c in p)
replaced_by_ = p.replace(' ', '-')
combined_queries = st... | flexible | {
"blob_id": "ec9de8d54113806ab327f05e077edefa74258adb",
"index": 2662,
"step-1": "<mask token>\n\n\nclass Solution:\n\n def __new__(self, p):\n nr_counts, nr_consonants, replaced = self.count_vowels_consonants(self,\n p)\n inversed = ''.join(c.lower() if c.isupper() else c.upper() for... | [
3,
4,
5,
6,
7
] |
# Generated by Django 2.0.13 on 2019-05-23 14:12
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('projects', '0001_initial'),
('users', '0003_user_projects'),
]
operations = [
migrations.RemoveField(
model_name='user'... | normal | {
"blob_id": "547935a67fb079e551534126534234ceb96ed0dd",
"index": 7648,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('projects', ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def update_customer(first_name, surname, cid, customer_repository):
customer = customer_repository.fetch_by_id(cid)
customer.first_name = first_name
customer.surname = surname
customer_repository.store(customer)
return customer
<|reserved_special_token_1|>
<|reserve... | flexible | {
"blob_id": "f5e60f2d384242b9675e756f67391ea09afcc262",
"index": 5408,
"step-1": "<mask token>\n\n\ndef update_customer(first_name, surname, cid, customer_repository):\n customer = customer_repository.fetch_by_id(cid)\n customer.first_name = first_name\n customer.surname = surname\n customer_reposito... | [
1,
2,
3,
4
] |
from django.shortcuts import render
from django.http import HttpResponseRedirect
from .forms import PostForm
from django.contrib.auth.decorators import login_required
from django.shortcuts import get_object_or_404
from .models import Post
from django.contrib import messages
# Create your views here.
@login_required
def... | normal | {
"blob_id": "4a2437d3d6ba549910bc30a67bf391b9bbafd25f",
"index": 6210,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@login_required\ndef post_create(request):\n \"\"\"\n\t\tThis makes sure that the form accpets a POST requests (of some data) or Nothing.\n\t\tWithout this the form would even acce... | [
0,
1,
2,
3,
4
] |
a=float.input('Valor da conta')
print('Valor da conta com 10%: R$',(a))
| normal | {
"blob_id": "d1ce6c081dce2e4bdb6087cd61d7f857dbb1348d",
"index": 8781,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Valor da conta com 10%: R$', a)\n",
"step-3": "a = float.input('Valor da conta')\nprint('Valor da conta com 10%: R$', a)\n",
"step-4": "a=float.input('Valor da conta')\nprint('... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class BasicBlock(layers.Layer):
<|reserved_special_token_0|>
def call(self, inputs, training=None):
out = self.conv1(inputs)
out = self.bn1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu2(out)
... | flexible | {
"blob_id": "e626a7f3f9241db8684c3b8c1bd79ea49e03490d",
"index": 8141,
"step-1": "<mask token>\n\n\nclass BasicBlock(layers.Layer):\n <mask token>\n\n def call(self, inputs, training=None):\n out = self.conv1(inputs)\n out = self.bn1(out)\n out = self.relu1(out)\n out = self.con... | [
6,
7,
8,
9,
11
] |
#!/usr/bin/python
import os;
import math;
# os.chdir('data/postgres/linux.env')
os.chdir('data/mysql/linux.env')
# os.chdir('data/mongo/linux.env')
col_time = 0;
col_read_ops = 1
col_read_err = 2
col_write_ops = 3
col_write_err = 4
class ColumnData:
def __init__(self, chart, title, data):
self.chart = ... | normal | {
"blob_id": "bb208d40ce098b05594aaf9c579f64b909738d52",
"index": 1067,
"step-1": "#!/usr/bin/python\n\nimport os;\nimport math;\n\n# os.chdir('data/postgres/linux.env')\nos.chdir('data/mysql/linux.env')\n# os.chdir('data/mongo/linux.env')\n\ncol_time = 0;\ncol_read_ops = 1\ncol_read_err = 2\ncol_write_ops = 3\nc... | [
0
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Greger Update Agent (GUA) module for the Greger Client Module
"""
__author__ = "Eric Sandbling"
__license__ = 'MIT'
__status__ = 'Development'
# System modules
import os, sys
import shutil
import logging
import subprocess
from threading import Event
from threading im... | normal | {
"blob_id": "a9b2a4d4924dcdd6e146ea346e71bf42c0259846",
"index": 593,
"step-1": "<mask token>\n\n\nclass GregerUpdateAgent(Thread):\n <mask token>\n <mask token>\n\n @property\n def localRevisionRecord(self):\n \"\"\"\n Get local revision record (.gcm)\n \"\"\"\n localLog ... | [
5,
6,
7,
8,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('****************************')
print('***** Caixa Eletronico *****')
print('****************************')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('*******************... | flexible | {
"blob_id": "44b6ee8488869da447882457897ce87b2fdea726",
"index": 7846,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('****************************')\nprint('***** Caixa Eletronico *****')\nprint('****************************')\n<mask token>\n",
"step-3": "<mask token>\nprint('*******************... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [m... | flexible | {
"blob_id": "2ec8d3853ea4a99d4e764c6c24d7b5a3afb64f63",
"index": 2830,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [migrations.sw... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class MazeEnv:
<|reserved_special_token_0|>
def __init__(self, GW, GH, SW, SH):
global GRID_WIDTH, GRID_HEIGHT, SCREEN_WIDTH, SCREEN_HEIGHT, BOX_WIDTH, BOX_HEIGHT
GRID_WIDTH = GW
GRID_HEIGHT = GH
SCREEN_WIDTH = SW
SCREEN_HEIGHT = SH
... | flexible | {
"blob_id": "751d2a07b97d080988c54511ca13a97a969e06bd",
"index": 6405,
"step-1": "<mask token>\n\n\nclass MazeEnv:\n <mask token>\n\n def __init__(self, GW, GH, SW, SH):\n global GRID_WIDTH, GRID_HEIGHT, SCREEN_WIDTH, SCREEN_HEIGHT, BOX_WIDTH, BOX_HEIGHT\n GRID_WIDTH = GW\n GRID_HEIGHT... | [
9,
10,
12,
13,
14
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestJobConfigHistory(WebAppTest):
def setUp(self):
super(TestJobConfigHistory, self).setUp()
config_path = os.getenv('CONFIG_PATH')
try:
yaml_contents = open('{}/job_config_history.... | flexible | {
"blob_id": "51bdbec732bebd73a84b52c6d1d39eead047d29e",
"index": 5349,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestJobConfigHistory(WebAppTest):\n\n def setUp(self):\n super(TestJobConfigHistory, self).setUp()\n config_path = os.getenv('CONFIG_PATH')\n try:\n ... | [
0,
2,
3,
4,
5
] |
def cubarea(l2,b2,h2):
print("Area of cuboid =",2*(l2+b2+h2))
def cubperimeter(l2,b2,h2):
print("Perimeter of cuboid =",4*(l2+b2+h2))
| normal | {
"blob_id": "45a85ff765833fd62fc1670404d8994818788707",
"index": 6873,
"step-1": "<mask token>\n",
"step-2": "def cubarea(l2, b2, h2):\n print('Area of cuboid =', 2 * (l2 + b2 + h2))\n\n\n<mask token>\n",
"step-3": "def cubarea(l2, b2, h2):\n print('Area of cuboid =', 2 * (l2 + b2 + h2))\n\n\ndef cubpe... | [
0,
1,
2,
3
] |
from werkzeug.security import check_password_hash, generate_password_hash
from datetime import datetime
from app import db
from app import login
from flask_login import UserMixin
@login.user_loader
def load_user(id):
return User.query.get(int(id))
class User(UserMixin, db.Model):
user_id = db.Column(db.Integer, p... | normal | {
"blob_id": "5cfdb1f6b99f59a83a9bd42b7daf3e016eee94a8",
"index": 2898,
"step-1": "<mask token>\n\n\nclass Post(db.Model):\n post_id = db.Column(db.Integer, primary_key=True, nullable=False)\n title = db.Column(db.String(50))\n body = db.Column(db.String(200))\n timestamp = db.Column(db.DateTime, inde... | [
5,
8,
10,
11,
13
] |
from src.produtos import *
class Estoque(object):
def __init__(self):
self.categorias = []
self.subcategorias = []
self.produtos = []
self.menu_estoque()
def save_categoria(self, categoria):
pass
def save_subcategorias(self, subcategoria):
pa... | normal | {
"blob_id": "9f3ca0d5a10a27d926a0f306665889418f8d6a0c",
"index": 5884,
"step-1": "<mask token>\n\n\nclass Estoque(object):\n <mask token>\n\n def save_categoria(self, categoria):\n pass\n <mask token>\n\n def save_produtos(self, produto):\n pass\n <mask token>\n\n def create_subca... | [
7,
11,
12,
17,
18
] |
import math
import random
import time
import numpy as np
class NeuralNetwork:
digits = [
[
1,1,1,1,1,
1,0,0,0,1,
1,0,0,0,1,
1,0,0,0,1,
1,1,1,1,1
],
[
0,0,1,0,0,
0,0,1,0,0,
0,0,1,0,0,
... | normal | {
"blob_id": "0af45914c8c111a42b0b9684f5f0ee19ef5eeb70",
"index": 7548,
"step-1": "<mask token>\n\n\nclass NeuralNetwork:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def withSeed(self, seed):\n self.seed = seed\n return self\n <mask token>\n\n def withMinErro... | [
7,
12,
13,
14,
19
] |
from util import *
def K_step(x):
if not x.shape:
return S.One
assert len(x.shape) == 1
n = x.shape[0]
if n == 2:
return x[1]
return Piecewise((1, Equal(n, 1)),
(x[1], Equal(n, 2)),
(K(x[:n - 1]) * x[n - 1] + K(x[:n - 2]), True))
K = Fun... | normal | {
"blob_id": "b00c07ee3cdba55800c9701b7b8b0e3c9079e9f8",
"index": 6272,
"step-1": "<mask token>\n\n\ndef K_step(x):\n if not x.shape:\n return S.One\n assert len(x.shape) == 1\n n = x.shape[0]\n if n == 2:\n return x[1]\n return Piecewise((1, Equal(n, 1)), (x[1], Equal(n, 2)), (K(x[:n... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/python3
"""
@author : Chris Phibbs
@created : Sunday Aug 30, 2020 14:05:56 AEST
@file : q3
"""
class Solution:
def minDays(self, grid: List[List[int]]) -> int:
# bfs - find 1, run bfs. Then loop through - if any other ones found then disconnected
i, j = 0... | normal | {
"blob_id": "cddd5deba0ddc59a604d2926bdc687716e08f226",
"index": 1557,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n <mask token>\n <mask token>\n\n def checkLand(self, grid, x, y):\n print(f... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def people_on_image(path_to_image):
color_map = [(255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255), (255,
255, 255), (255, 255, 255), (255,... | flexible | {
"blob_id": "2193c97b7f1fcf204007c2528ecc47cbf3c67e81",
"index": 9992,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef people_on_image(path_to_image):\n color_map = [(255, 255, 255), (255, 255, 255), (255, 255, 255), (255, \n 255, 255), (255, 255, 255), (255, 255, 255), (255, 255, 255), ... | [
0,
1,
2,
3
] |
$ pip install "<package_name> >= 1.1"
| normal | {
"blob_id": "8010c0d53af6d428f29ff3ce63bcd6b5b811b051",
"index": 3456,
"step-1": "$ pip install \"<package_name> >= 1.1\"\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def interpret(trees):
for tree in trees:
nodetype = tree[0]
if nodetype == 'word-element':
graphics.word(tree[1])
elif nodetype == 'tag-element':
tagname = tree[1]
... | flexible | {
"blob_id": "f3b3bee494493263f8b00827e6f3ff3a1dcd8c37",
"index": 6144,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef interpret(trees):\n for tree in trees:\n nodetype = tree[0]\n if nodetype == 'word-element':\n graphics.word(tree[1])\n elif nodetype == 'tag-el... | [
0,
1,
2,
3
] |
# p.85 (문자 갯수 카운팅)
message = \
'It was a bright cold day in April, and the clocks were striking thirteen.'
print(message, type(message))
msg_dict = dict() #빈 dict() 생성
for msg in message:
print(msg, message.count(msg))
msg_dict[msg] = message.count(msg)
print(msg_dict)
| normal | {
"blob_id": "20671470c087719fa9ea8ffa25be55e9ade67681",
"index": 5373,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(message, type(message))\n<mask token>\nfor msg in message:\n print(msg, message.count(msg))\n msg_dict[msg] = message.count(msg)\nprint(msg_dict)\n",
"step-3": "message = (\... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
subprocess.call(options)
subprocess.call([f'./bin/{name}'])
<|reserved_special_token_1|>
<|reserved_special_token_0|>
path = sys.argv[1]
name, ext = os.path.splitext(path)
options = ['g++', '-O3', 'src/' + path, '-o', f'./bin/{... | flexible | {
"blob_id": "5dd79f8ebd74099871d4367cafd83359c4f24e26",
"index": 5385,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsubprocess.call(options)\nsubprocess.call([f'./bin/{name}'])\n",
"step-3": "<mask token>\npath = sys.argv[1]\nname, ext = os.path.splitext(path)\noptions = ['g++', '-O3', 'src/' + path,... | [
0,
1,
2,
3,
4
] |
a=[1,2,3,4,5]
max=0
for i in a:
if i>=max:
max=i
elif i<=min:
min=i
print max
print min
| normal | {
"blob_id": "65da68d33aa382ed6deeff3c66a063ee299c2567",
"index": 1448,
"step-1": "a=[1,2,3,4,5]\nmax=0\nfor i in a:\n\tif i>=max:\n\t\tmax=i\n\telif i<=min:\n\t\tmin=i\nprint max\nprint min\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
'''
'''
import numpy as np
from scipy.spatial import distance
def synonym_filter(WordVectors_npArray, WordLabels_npArray):
'''
'''
pass
def synonym_alternatives_range(WordVectors_npArray,
AlternativesVectorOne_npArray,
Alternati... | normal | {
"blob_id": "ea0a59953f2571f36e65f8f958774074b39a9ae5",
"index": 6996,
"step-1": "<mask token>\n\n\ndef synonym_alternatives_range(WordVectors_npArray,\n AlternativesVectorOne_npArray, AlternativesVectorTwo_npArray,\n AlternativesVectorThree_npArray, AlternativesVectorFour_npArray):\n \"\"\"\n \"\"\"... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def log_all_ships(myMap):
logging.debug('Logging all ships:')
for ship_id, ship in myMap.data_ships[myMap.my_id].items():
logging.debug('ship_id: {}'.format(ship_id))
for k, v in ship.items():
logging.debug(' {}: {}'.format(k, v))
def log_all_plan... | flexible | {
"blob_id": "879bb8d67c0e1e8b125ac5994fcb142e3366c9d8",
"index": 9094,
"step-1": "<mask token>\n\n\ndef log_all_ships(myMap):\n logging.debug('Logging all ships:')\n for ship_id, ship in myMap.data_ships[myMap.my_id].items():\n logging.debug('ship_id: {}'.format(ship_id))\n for k, v in ship.i... | [
3,
6,
7,
11,
12
] |
import os
import mysql.connector
import time
from flask import Flask, render_template
app = Flask(__name__)
def dbconnect():
return mysql.connector.connect(user= , password= , host="mysqlshereen.mysql.database.azure.com", port=3306, database='test')
@app.route('/result', methods=['POST', 'GET'])
def query():
... | normal | {
"blob_id": "3314ffdbc2f10170176c590aebf49c416bcc8856",
"index": 2136,
"step-1": "import os\n\nimport mysql.connector\nimport time\nfrom flask import Flask, render_template\n\napp = Flask(__name__)\n\ndef dbconnect():\n\n return mysql.connector.connect(user= , password= , host=\"mysqlshereen.mysql.database.az... | [
0
] |
# -*- coding: utf-8 -*-
#########################################################################
## This scaffolding model makes your app work on Google App Engine too
## File is released under public domain and you can use without limitations
#########################################################################
... | normal | {
"blob_id": "93c465f017542cfe9cbc55da0ae5a9e34663cf32",
"index": 1978,
"step-1": "# -*- coding: utf-8 -*-\n\n#########################################################################\n## This scaffolding model makes your app work on Google App Engine too\n## File is released under public domain and you can use w... | [
0
] |
<|reserved_special_token_0|>
class ServicoForm(forms.ModelForm):
<|reserved_special_token_0|>
class Meta:
model = Servico
class ServicosAdmin(CustomModelAdmin):
list_display = 'imagem_icone', 'titulo', 'intro'
list_display_links = 'titulo', 'intro'
search_fields = ['titulo', 'intro', '... | flexible | {
"blob_id": "caac9dfc7d52607c2af67ddc03a3a7bdae9911bb",
"index": 8204,
"step-1": "<mask token>\n\n\nclass ServicoForm(forms.ModelForm):\n <mask token>\n\n\n class Meta:\n model = Servico\n\n\nclass ServicosAdmin(CustomModelAdmin):\n list_display = 'imagem_icone', 'titulo', 'intro'\n list_displ... | [
13,
14,
15,
16,
19
] |
import math
print ("programa que calcula hipotenusa tomando el valor de los catetos en tipo double---")
print ("------------------------------------------------------------------------")
print (" ")
catA = float(input("igrese el valor del cateto A"))
catB = float(input("ingrese el valor del catebo B"))
def calcularHi... | normal | {
"blob_id": "af217d0cc111f425282ee21bd47d9007a69a6239",
"index": 6297,
"step-1": "<mask token>\n\n\ndef calcularHipotenusa(catA, catB):\n hipotenusa = catA ** 2 + catB ** 2\n hipotenusa = math.sqrt(hipotenusa)\n hipotenusa = float(hipotenusa)\n print('la hipotenusa es: ', hipotenusa)\n\n\n<mask token... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class ray:
def __init__(self, *args):
if len(args) == 0:
self.A = vec3(0, 0, 0)
self.B = vec3(1, 0, 0)
elif len(args) == 2:
if type(args[0]) != vec3 or type(args[1]) != vec3:
raise ValueError('Expected two vec3s')
... | flexible | {
"blob_id": "a73e3a07ab0ebb90fa744d3dfc8d9da119f99283",
"index": 2070,
"step-1": "<mask token>\n\n\nclass ray:\n\n def __init__(self, *args):\n if len(args) == 0:\n self.A = vec3(0, 0, 0)\n self.B = vec3(1, 0, 0)\n elif len(args) == 2:\n if type(args[0]) != vec3 ... | [
4,
5,
6,
7,
8
] |
from unv.app.base import Application
def multiply():
print('multiply', 2 * 2)
def setup(app: Application):
app.register_run_task(multiply)
| normal | {
"blob_id": "760a62a94347171eb9e40015c0c43d72df8f4fc8",
"index": 1463,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef setup(app: Application):\n app.register_run_task(multiply)\n",
"step-3": "<mask token>\n\n\ndef multiply():\n print('multiply', 2 * 2)\n\n\ndef setup(app: Application):\n ... | [
0,
1,
2,
3
] |
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution:
# @param head, a ListNode
# @return a ListNode
def insertionSortList(self, head):
if not head:
return head
fh = List... | normal | {
"blob_id": "c234031fa6d43c19515e27c5b12f8e8338f24a1c",
"index": 6412,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def insertionSortList(self, head):\n if not head:\n return head\n fh = ListNode(0)\n fh.next = ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
BOT_NAME = ['lg']
SPIDER_MODULES = ['lg.spiders']
NEWSPIDER_MODULE = 'lg.spiders'
DOWNLOAD_DELAY = 0.1
LOG_LEVEL = 'WARNING'
<|reserved_special_token_1|>
# coding: utf-8
BOT_NAME = ['lg']
SPIDER_MODULES = ['lg.spiders']
NEWSPIDER_MODULE = 'lg.spiders'
D... | flexible | {
"blob_id": "bed3d83f682404719a95be360cdd74be9dc87991",
"index": 3718,
"step-1": "<mask token>\n",
"step-2": "BOT_NAME = ['lg']\nSPIDER_MODULES = ['lg.spiders']\nNEWSPIDER_MODULE = 'lg.spiders'\nDOWNLOAD_DELAY = 0.1\nLOG_LEVEL = 'WARNING'\n",
"step-3": "# coding: utf-8\n\nBOT_NAME = ['lg']\n\nSPIDER_MODULES ... | [
0,
1,
2
] |
# coding: utf-8
# # Read Bathy data from ERDDAP
# In[ ]:
get_ipython().system(u'conda install basemap --yes')
# In[1]:
import numpy as np
import matplotlib.pyplot as plt
import urllib
import netCDF4
from mpl_toolkits.basemap import Basemap
# In[2]:
# Definine the domain of interest
minlat = 42
maxlat = 45
min... | normal | {
"blob_id": "6d0340a08701b0c4f34e9b833bca27cf455d682d",
"index": 827,
"step-1": "\n# coding: utf-8\n\n# # Read Bathy data from ERDDAP\n\n# In[ ]:\n\nget_ipython().system(u'conda install basemap --yes')\n\n\n# In[1]:\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport urllib\nimport netCDF4\nfrom mpl_t... | [
0
] |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#
# Author : cold
# E-mail : wh_linux@126.com
# Date : 13/09/05 11:16:58
# Desc :
#
import twqq
from setuptools import setup
requires = ["tornado", "pycurl", "tornadohttpclient"]
packages = ["twqq"]
entry_points = {
}
setup(
name = "twqq",
... | normal | {
"blob_id": "9492142a569da1d21b1927e79d97f9cf6276efdc",
"index": 2800,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='twqq', version=twqq.__version__, description=\n 'An asynchronous webqq client library based on tornado',\n long_description=open('README.rst').read(), author='cold', aut... | [
0,
1,
2,
3,
4
] |
# Part 1 - Build the CNN
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
## Initialize the CNN
classifier = Sequential()
## Step 1 - Convolution Layer
classifier.add(Convolution2D(32, 3, 3,... | normal | {
"blob_id": "b0aeede44a4b54006cf0b7d541d5b476a7178a93",
"index": 6155,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nclassifier.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(64,\n 64, 3), activation='relu'))\nclassifier.add(MaxPooling2D(pool_size=(2, 2)))\nclassifier.add(Convolution2D(... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "d09984c6e6a0ce82389dbbbade63507e9687355d",
"index": 771,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('Pages', '001... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class PasswordRecoveryForm(forms.Form):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class TokenRequestForm(forms.Form):
email = forms.EmailField()
def send(self):
url = '{0}/users/{1}/password'.format(settings.TSURU... | flexible | {
"blob_id": "27fc11ae68531c7dbafdcf134f0eef019210e2de",
"index": 8347,
"step-1": "<mask token>\n\n\nclass PasswordRecoveryForm(forms.Form):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass TokenRequestForm(forms.Form):\n email = forms.EmailField()\n\n def send(self):\n url = '{0}/use... | [
14,
15,
18,
19,
20
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def getKeys(f):
keys = {}
f = open(f, 'r')
for line in f:
apiInfo = line.split(',')
keys[apiInfo[0]] = apiInfo[1].strip(string.whitespace)
keys.pop('apiName', None)
return keys
<|reserved_sp... | flexible | {
"blob_id": "3653c6fce33467600a3eea72578ed995606bfc03",
"index": 4100,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef getKeys(f):\n keys = {}\n f = open(f, 'r')\n for line in f:\n apiInfo = line.split(',')\n keys[apiInfo[0]] = apiInfo[1].strip(string.whitespace)\n keys.p... | [
0,
1,
2,
3
] |
# coding: utf-8
"""
Negotiation API
The <b>Negotiations API</b> gives sellers the ability to proactively send discount offers to buyers who have shown an \"interest\" in their listings. <br><br>By sending buyers discount offers on listings where they have shown an interest, sellers can increase the velocity ... | normal | {
"blob_id": "a93818440410bde004f0203f18112fa1b666959c",
"index": 9615,
"step-1": "<mask token>\n\n\nclass OfferApi(object):\n <mask token>\n <mask token>\n <mask token>\n\n def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id,\n **kwargs):\n \"\"\"find_eligible_items # ... | [
4,
5,
6,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if sys.hexversion < 50331648:
from .foo import foo
<|reserved_special_token_1|>
import sys
if sys.hexversion < 50331648:
from .foo import foo
<|reserved_special_token_1|>
import sys
if sys.hexversion < 0x03000000:
... | flexible | {
"blob_id": "485729398b51bebd16f38800c6100289b7b0b347",
"index": 9023,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif sys.hexversion < 50331648:\n from .foo import foo\n",
"step-3": "import sys\nif sys.hexversion < 50331648:\n from .foo import foo\n",
"step-4": "\nimport sys\n\nif sys.hexver... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
plt.xlabel('Freq (in rad/s)')
plt.ylabel('Phase (in deg)')
plt.title('Phase plot')
plt.semilogx(w, phase1, label='With Controller')
plt.semilogx(w, phase2, label='Without Controller')
plt.grid()
plt.legend()
plt.show()
<|reserve... | flexible | {
"blob_id": "84e84d9f35702c2572ad5e7daa92a271674986dc",
"index": 3882,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.xlabel('Freq (in rad/s)')\nplt.ylabel('Phase (in deg)')\nplt.title('Phase plot')\nplt.semilogx(w, phase1, label='With Controller')\nplt.semilogx(w, phase2, label='Without Controller')... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class StyblinskiTang(Function2D):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def grad(self, x):
""" Grad function. """
g = np.zeros(x.shape)
g[... | flexible | {
"blob_id": "5d8715dd02feff4e13919858051abeb5b6828011",
"index": 6798,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass StyblinskiTang(Function2D):\n <mask token>\n <mask token>\n <mask token>\n\n def grad(self, x):\n \"\"\" Grad function. \"\"\"\n g = np.zeros(x.shape)\... | [
0,
3,
6,
7,
8
] |
#파이썬 심화
#클래스 메소드, 인스턴스 메소드, 스테이틱 메소드
# 기본 인스턴스 메소드
class Student(object):
"""
Student Class
Author : Kim
Date : 2020.11.07
Description : Class, Static, Instance Method
"""
#Class Variable
tuition_per = 1.0
def __init__(self, id, first_name, last_name, email, grade... | normal | {
"blob_id": "f507fbe7c92134c0a7149aafe7de88debebd42f5",
"index": 7760,
"step-1": "class Student(object):\n <mask token>\n <mask token>\n <mask token>\n\n def full_name(self):\n return '{} {}'.format(self._first_name, self._last_name)\n\n def detail_info(self):\n return 'Student Detai... | [
5,
7,
11,
13,
16
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
ours2.append(mine)
ours2.append(yours)
print(ours1)
print(ours2)
<|reserved_special_token_0|>
print(ours1)
print(ours2)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
yours = ['Yale', 'MIT', 'Berkeley']
mine = ['Harv... | flexible | {
"blob_id": "bf65d4a4e066e3e06b888d4b9ed49e10e66b4e78",
"index": 8145,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nours2.append(mine)\nours2.append(yours)\nprint(ours1)\nprint(ours2)\n<mask token>\nprint(ours1)\nprint(ours2)\n",
"step-3": "<mask token>\nyours = ['Yale', 'MIT', 'Berkeley']\nmine = ['... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
async def add_time(chat, time):
return col.insert_one({'chat': chat, 'time': time})
async def get_time(chat):
return col.find_one({'chat': chat})
async def update_time(chat, time):
return col.update_one({'chat': ... | flexible | {
"blob_id": "e4ce10f5db56e4e2e1988da3cee542a4a09785a8",
"index": 5381,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nasync def add_time(chat, time):\n return col.insert_one({'chat': chat, 'time': time})\n\n\nasync def get_time(chat):\n return col.find_one({'chat': chat})\n\n\nasync def update_... | [
0,
1,
2,
3
] |
from graphics.rectangle import *
from graphics.circle import *
from graphics.DGraphics.cuboid import *
from graphics.DGraphics.sphere import *
print ("------rectangle-------")
l=int(input("enter length : "))
b=int(input("enter breadth : "))
print("area of rectangle : ",RectArea(1,b))
print("perimeter of rectang... | normal | {
"blob_id": "f275085a2e4e3efc8eb841b5322d9d71f2e43846",
"index": 7998,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('------rectangle-------')\n<mask token>\nprint('area of rectangle : ', RectArea(1, b))\nprint('perimeter of rectangle : ', Rectperimeter(1, b))\nprint()\nprint('-------circle-------... | [
0,
1,
2,
3,
4
] |
import scipy.sparse
from multiprocessing.sharedctypes import Array
from ctypes import c_double
import numpy as np
from multiprocessing import Pool
import matplotlib.pyplot as plt
from time import time
import scipy.io as sio
import sys
# np.random.seed(1)
d = 100
n = 100000
k=10
learning_rate = 0.4
T_freq = 100
num_t... | normal | {
"blob_id": "bf04bf41f657a6ada4777fe5de98d6a68beda9d3",
"index": 9769,
"step-1": "<mask token>\n\n\ndef getSyntheticData(n, d, k):\n mean = np.array([0] * d)\n alpha = 0.8\n cov_diag = [(alpha ** i) for i in range(d)]\n covariance = np.diag(cov_diag)\n truth = np.sum(cov_diag[:k])\n samples = n... | [
2,
5,
8,
9,
10
] |
from django.contrib import admin
from django.urls import path
from . import views
urlpatterns = [
path('', views.skincare, name="skin"),
path('productSearch/', views.productSearch, name="productSearch"),
path('detail/', views.detail, name="detail"),
] | normal | {
"blob_id": "c31c59d172b2b23ca4676be0690603f33b56f557",
"index": 4867,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', views.skincare, name='skin'), path('productSearch/',\n views.productSearch, name='productSearch'), path('detail/', views.\n detail, name='detail')]\n",
"st... | [
0,
1,
2,
3
] |
_registry = []
def registry(name):
_registry.append(name)
def registry_names():
return iter(_registry)
| normal | {
"blob_id": "51642dbb210600f9ca4e035fb884fbdda030fd04",
"index": 1491,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef registry_names():\n return iter(_registry)\n",
"step-3": "<mask token>\n\n\ndef registry(name):\n _registry.append(name)\n\n\ndef registry_names():\n return iter(_regis... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def longest(s1, s2):
s = s1 + s2
st = ''.join(sorted(set(s)))
return st
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def longest(s1, s2):
s = s1 + s2
st = ''.join(sorted(set(s)))
return st
longest('xyaabbbccccdefww'... | flexible | {
"blob_id": "7d54d5fd855c7c03d2d4739e8ad4f9ab8772ca2b",
"index": 3977,
"step-1": "<mask token>\n",
"step-2": "def longest(s1, s2):\n s = s1 + s2\n st = ''.join(sorted(set(s)))\n return st\n\n\n<mask token>\n",
"step-3": "def longest(s1, s2):\n s = s1 + s2\n st = ''.join(sorted(set(s)))\n re... | [
0,
1,
2,
3
] |
import unittest
from battleline.model.Formation import Formation, FormationInvalidError
class TestFormation(unittest.TestCase):
def test_formation_with_less_than_three_cards_is_considered_invalid(self):
self.assertRaisesRegexp(
FormationInvalidError, "Formation must have 3 cards", Formation, ... | normal | {
"blob_id": "0ce69b7ce99b9c01892c240d5b268a9510af4503",
"index": 1648,
"step-1": "<mask token>\n\n\nclass TestFormation(unittest.TestCase):\n <mask token>\n\n def test_formation_with_more_than_three_cards_is_considered_invalid(self):\n self.assertRaisesRegexp(FormationInvalidError,\n 'For... | [
18,
21,
22,
25,
26
] |
from tqdm import trange
import numpy as np
class GPTD_fixedGrid:
def __init__(self, env, sigma0, gamma, kernel, D, V_mu=[]):
self.env = env
self.gamma = gamma
self.sigma0 = sigma0
self.kernel = kernel.kernel
if (not V_mu):
V_mu = lambda s: np.zeros((s.sh... | normal | {
"blob_id": "92eaceb46974ba3a5944300139d5929d44673181",
"index": 1223,
"step-1": "<mask token>\n\n\nclass GPTD_fixedGrid:\n\n def __init__(self, env, sigma0, gamma, kernel, D, V_mu=[]):\n self.env = env\n self.gamma = gamma\n self.sigma0 = sigma0\n self.kernel = kernel.kernel\n ... | [
3,
5,
6,
7,
8
] |
from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.pipeline import make_pipeline... | normal | {
"blob_id": "53cbc3ca3a34a8aafa97d6964337cfabb1bebac5",
"index": 8957,
"step-1": "from sklearn.datasets import fetch_20newsgroups\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import HashingVectorizer\nfrom sklea... | [
0
] |
# Generated by Django 3.1.5 on 2021-02-24 18:34
from django.db import migrations, models
import stdimage.models
class Migration(migrations.Migration):
dependencies = [
('Site', '0004_arquivopdf'),
]
operations = [
migrations.CreateModel(
name='historico',
fields=... | normal | {
"blob_id": "321147f2e2d8caf6d9224e2a8969f51ded48baf7",
"index": 8130,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('Site', '000... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class CreateArchive(QtWidgets.QDialog):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def create_components(self):
self.option_widget = QtWidgets.QWidget()
self.name_lbl = QtWidgets.QLabel('Nazwa')
self.name_edit = QtWidgets.QLineEdit('unti... | flexible | {
"blob_id": "7a41826f65f2f55b4c678df2ac06027df6ca50d4",
"index": 3623,
"step-1": "<mask token>\n\n\nclass CreateArchive(QtWidgets.QDialog):\n <mask token>\n <mask token>\n\n def create_components(self):\n self.option_widget = QtWidgets.QWidget()\n self.name_lbl = QtWidgets.QLabel('Nazwa')\... | [
9,
10,
15,
16,
18
] |
<|reserved_special_token_0|>
def get_youtube_handler():
"""Return the API Youtube object."""
options = {}
home = os.path.expanduser('~')
default_credentials = os.path.join(home, '.youtube-upload-credentials.json'
)
client_secrets = os.path.join(home, '.client_secrets.json')
credentials... | flexible | {
"blob_id": "65d08fe1a3f6e5cc2458209706307513d808bdb2",
"index": 3824,
"step-1": "<mask token>\n\n\ndef get_youtube_handler():\n \"\"\"Return the API Youtube object.\"\"\"\n options = {}\n home = os.path.expanduser('~')\n default_credentials = os.path.join(home, '.youtube-upload-credentials.json'\n ... | [
4,
5,
6,
7,
8
] |
__author__ = 'piotrek'
import os
import zipfile
import tarfile
from PyQt5 import QtWidgets
from PyQt5 import QtGui
from PyQt5 import QtCore
from Widgets.list_view import ListView
from Threads.PackThread import PackThread
class CreateArchive(QtWidgets.QDialog):
def __init__(self, model, index, path, parent=Non... | normal | {
"blob_id": "7a41826f65f2f55b4c678df2ac06027df6ca50d4",
"index": 3623,
"step-1": "<mask token>\n\n\nclass CreateArchive(QtWidgets.QDialog):\n <mask token>\n <mask token>\n\n def create_components(self):\n self.option_widget = QtWidgets.QWidget()\n self.name_lbl = QtWidgets.QLabel('Nazwa')\... | [
9,
10,
15,
16,
18
] |
<|reserved_special_token_0|>
@app.route('/')
def home():
"""List all available api routes."""
return (
f'Available Routes:<br/>/api/v1.0/precipitation<br/>/api/v1.0/stations<br/>/api/v1.0/tobs<br/>/api/v1.0/<start><br/>/api/v1.0/<start>/<end><br/>'
)
<|reserved_special_token_0|>
@app.route... | flexible | {
"blob_id": "7ab964352c1d51b70e3a1a7bf0a624f2d96cfd55",
"index": 8168,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef home():\n \"\"\"List all available api routes.\"\"\"\n return (\n f'Available Routes:<br/>/api/v1.0/precipitation<br/>/api/v1.0/stations<br/>/api/v1.0/tobs<br/>/api/v1.0/<start><b... | [
3,
6,
7,
9,
10
] |
<|reserved_special_token_0|>
class FDA_node(object):
<|reserved_special_token_0|>
def grow(self):
self.right = FDA_node()
self.left = FDA_node()
def find_optimal_param(self, x, y):
self.m = self.method.find_optimal_param(x, y)
if self.left != None and self.right != None:
... | flexible | {
"blob_id": "784b51c05dc7b5e70016634e2664c9ec25b8a65a",
"index": 6506,
"step-1": "<mask token>\n\n\nclass FDA_node(object):\n <mask token>\n\n def grow(self):\n self.right = FDA_node()\n self.left = FDA_node()\n\n def find_optimal_param(self, x, y):\n self.m = self.method.find_optim... | [
5,
6,
9,
11,
12
] |
<|reserved_special_token_0|>
def from_url(url: str) ->Image.Image:
api_response = requests.get(url).content
response_bytes = BytesIO(api_response)
return Image.open(response_bytes)
def from_file(path: str) ->Union[Image.Image, None]:
if os.path.exists(path):
return Image.open(path)
else:... | flexible | {
"blob_id": "f2bb44600f011a205c71985ad94c18f7e058634f",
"index": 8,
"step-1": "<mask token>\n\n\ndef from_url(url: str) ->Image.Image:\n api_response = requests.get(url).content\n response_bytes = BytesIO(api_response)\n return Image.open(response_bytes)\n\n\ndef from_file(path: str) ->Union[Image.Image... | [
2,
3,
4,
5,
6
] |
import numpy as np
from .metrics import r2_score
class LinearRegression:
def __init__(self):
self.coef_ = None # 系数
self.interception_ = None # 截距
self._theta = None
def fit_normal(self, X_train, y_train):
assert X_train.shape[0] == y_train.shape[0], ""
#!!!impor... | normal | {
"blob_id": "e47e614c88c78fb6e8ff4098ea2b89d21bfa9684",
"index": 6935,
"step-1": "<mask token>\n\n\nclass LinearRegression:\n\n def __init__(self):\n self.coef_ = None\n self.interception_ = None\n self._theta = None\n <mask token>\n\n def fit_gd(self, X_train, y_train, eta=0.01, n_... | [
5,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = []... | flexible | {
"blob_id": "4a118f9081a8b3baf0b074c8dc14eaeef4559c08",
"index": 6684,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = []\n operat... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(response.text)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
response = requests.get(
'https://any-api.com:8443/https://rbaskets.in/api/version')
print(response.text)
<|reserved_special_token_1|>
import... | flexible | {
"blob_id": "ab36b3d418be67080e2efaba15edc1354386e191",
"index": 6888,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(response.text)\n",
"step-3": "<mask token>\nresponse = requests.get(\n 'https://any-api.com:8443/https://rbaskets.in/api/version')\nprint(response.text)\n",
"step-4": "import... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def get_diabetes_data(target='progression'):
"""Get the SKLearn Diabetes regression dataset, formatted as a DataFrame
Parameters
----------
target: String, default='progression'
What to name the column in `df` that contains the target output values
Returns
... | flexible | {
"blob_id": "285ca945696b32160175f15c4e89b3938f41ebf4",
"index": 2172,
"step-1": "<mask token>\n\n\ndef get_diabetes_data(target='progression'):\n \"\"\"Get the SKLearn Diabetes regression dataset, formatted as a DataFrame\n\n Parameters\n ----------\n target: String, default='progression'\n W... | [
1,
2,
3,
4,
5
] |
from pirates.teleport.AreaTeleportActor import AreaTeleportActor
class DoorTeleportActor(AreaTeleportActor):
pass
| normal | {
"blob_id": "b679444fde7cd8eb819443922f37ee54c0f29de4",
"index": 424,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass DoorTeleportActor(AreaTeleportActor):\n pass\n",
"step-3": "from pirates.teleport.AreaTeleportActor import AreaTeleportActor\n\n\nclass DoorTeleportActor(AreaTeleportActor):... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class SessionRun:
def __init__(self, sessionId, cypher, params):
self.sessionId = sessionId
self.cypher = cypher
self.params = params
class SessionReadTransaction:
def __init__(self, sessionId):
self.sessionId = sessionId
<|reserved_special_to... | flexible | {
"blob_id": "dfcb095b26a21ba0c8ccc2a2c664bcfab29b8351",
"index": 8214,
"step-1": "<mask token>\n\n\nclass SessionRun:\n\n def __init__(self, sessionId, cypher, params):\n self.sessionId = sessionId\n self.cypher = cypher\n self.params = params\n\n\nclass SessionReadTransaction:\n\n def... | [
14,
16,
17,
19,
23
] |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 19 05:29:19 2020
@author: Gaurav
"""
from tensorflow.keras.models import load_model
import cv2
import os
from tensorflow.keras.preprocessing.image import img_to_array
import numpy as np
model=load_model('E:/AI Application Implementation/trained_model/Classifi... | normal | {
"blob_id": "c3e2bd635a7ff558ed56e7fb35e8b10e1c660c88",
"index": 6804,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in arr:\n img = cv2.imread(i)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = cv2.resize(img, (32, 32))\n img = img_to_array(img)\n img = np.expand_dims(img, axi... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def sketch(img, threshold=15):
"""
素描画生成
param img: Image实例
param threshold: 介于0到100
:return:
"""
if threshold < 0:
threshold = 0
if threshold > 100:
threshold = 100
if len(img.s... | flexible | {
"blob_id": "065354d2a8fd8a75e16bf85f624b12641377029a",
"index": 8568,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef sketch(img, threshold=15):\n \"\"\"\n 素描画生成\n param img: Image实例\n param threshold: 介于0到100\n :return:\n \"\"\"\n if threshold < 0:\n threshold = 0\n ... | [
0,
1,
2,
3
] |
import sys
import os
arcpy_path = [r'D:\software\ArcGIS\python 27\ArcGIS10.2\Lib\site-packages',
r'D:\software\ArcGIS\Desktop 10.2\Desktop10.2\arcpy',
r'D:\software\ArcGIS\Desktop 10.2\Desktop10.2\bin',
r'D:\software\ArcGIS\Desktop 10.2\Desktop10.2\ArcToolbox\Scripts']
sys.pa... | normal | {
"blob_id": "eab2cdd92d3be5760f13e747b05ca902eaf9aca8",
"index": 8287,
"step-1": "<mask token>\n\n\ndef CreateCGCS2000prj(shpPath):\n body = (\n 'GEOGCS[\"CGCS_2000\",DATUM[\"D_2000\",SPHEROID[\"S_2000\",6378137.0,298.2572221010041]],PRIMEM[\"Greenwich\",0.0],UNIT[\"Degree\",0.0174532925199433]]'\n ... | [
1,
3,
6,
9,
10
] |
<|reserved_special_token_0|>
def corr2d(X, K):
"""
定义二维互相关运算函数
:param X:输入数组
:param K: 核数组
:return:二维互相关的运算结果
"""
h, w = K.shape
Y = tf.Variable(tf.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1)))
for i in range(Y.shape[0]):
for j in range(Y.shape[1]):
Y[i, j].a... | flexible | {
"blob_id": "3f473701b186b5287258ba74e478cccdad0f29bf",
"index": 2463,
"step-1": "<mask token>\n\n\ndef corr2d(X, K):\n \"\"\"\n 定义二维互相关运算函数\n :param X:输入数组\n :param K: 核数组\n :return:二维互相关的运算结果\n \"\"\"\n h, w = K.shape\n Y = tf.Variable(tf.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1)))... | [
1,
2,
3,
4,
5
] |
def _get_single_variable(self, name, shape=None, dtype=dtypes.float32, initializer=None, regularizer=None, partition_info=None, reuse=None, trainable=True, collections=None, caching_device=None, validate_shape=True, use_resource=None):
'Get or create a single Variable (e.g. a shard or entire variable).\n\n See t... | normal | {
"blob_id": "51ef1c0f6a17e12b2324a80f962b2ce47cc05bcc",
"index": 1348,
"step-1": "<mask token>\n",
"step-2": "def _get_single_variable(self, name, shape=None, dtype=dtypes.float32,\n initializer=None, regularizer=None, partition_info=None, reuse=None,\n trainable=True, collections=None, caching_device=No... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def suck(f):
hamdevall = spamdevall = 0.0, 0.0
cost = 0.0
bestcost = 0.0
fp = 0
fn = 0
un = 0
fpp = 0.0
fnp = 0.0
unp = 0.0
htest = 0
stest = 0
get = f.readline
while 1:
line = get()
if line.startswith('-> <stat> tested')... | flexible | {
"blob_id": "4e94e9e2b45d3786aa86be800be882cc3d5a80b5",
"index": 8328,
"step-1": "<mask token>\n\n\ndef suck(f):\n hamdevall = spamdevall = 0.0, 0.0\n cost = 0.0\n bestcost = 0.0\n fp = 0\n fn = 0\n un = 0\n fpp = 0.0\n fnp = 0.0\n unp = 0.0\n htest = 0\n stest = 0\n get = f.r... | [
1,
2,
3,
4,
5
] |
import cv2
import numpy as np
import pandas as pd
import tkinter as tk
import random
from tkinter import *
from tkinter import ttk
from tkinter import messagebox
from tkinter import Scale,Tk
from tkinter.ttk import Notebook
refPt = []
PtBGR=[]
r=[]
g=[]
b=[]
refPt = []
Serial=[]
PtBGR=[]
r1=[]
r2=[]
r3=[]
r4=[]
rate=[... | normal | {
"blob_id": "a126b1775ffe1ba1aebc288ce17fac8ada0b0756",
"index": 312,
"step-1": "<mask token>\n\n\ndef quitScreen():\n messagebox.showinfo('collecting data', '點擊視窗開始分析')\n root.destroy()\n root2 = Tk()\n root2.destroy()\n\n\ndef getTextInput():\n global result, result2\n result = text.get(1.0, ... | [
5,
6,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def backupToZip(folder):
folder = os.path.abspath(folder)
os.chdir(folder)
number = 1
while True:
zipFilename = os.path.basename(folder) + '_' + str(number) + '.zip'
if not os.path.exists(zipFilen... | flexible | {
"blob_id": "7af19f69e6c419649a5999f594118ad13833a537",
"index": 7398,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef backupToZip(folder):\n folder = os.path.abspath(folder)\n os.chdir(folder)\n number = 1\n while True:\n zipFilename = os.path.basename(folder) + '_' + str(numbe... | [
0,
1,
2,
3,
4
] |
positivo = float(1.0000001)
negativo = float(-1.000001)
print(negativo, positivo)
b_pos = bin(positivo)
b_neg = bin(negativo)
print(b_neg, b_pos)
| normal | {
"blob_id": "5c908697000247056bb63a443f837eef88b4c957",
"index": 9196,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(negativo, positivo)\n<mask token>\nprint(b_neg, b_pos)\n",
"step-3": "positivo = float(1.0000001)\nnegativo = float(-1.000001)\nprint(negativo, positivo)\nb_pos = bin(positivo)\nb... | [
0,
1,
2
] |
import pygame
import numpy as np
import glob
from entities.base import AnimatedSprite
images_path = sorted(glob.glob('./resources/trophy_sparkle_*.png'))
trophy_im_dict = {'sparkle':[pygame.transform.scale(pygame.image.load(img_path),(400,400)) for img_path in images_path]}
class Trophy(AnimatedSprite):
def __in... | normal | {
"blob_id": "883cb1e3ea227bb5ac5aa3b4348336ab1a7fba70",
"index": 3476,
"step-1": "<mask token>\n\n\nclass Trophy(AnimatedSprite):\n\n def __init__(self, position, image_dict, hold_for_n_frames=3):\n super().__init__(position, image_dict, hold_for_n_frames)\n self.initial_position = position\n ... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# jan 2014 bbb garden shield attempt
# AKA
'''
Sensors:
analog level sensor, pin AIN0
TMP102 i2c temperature sensor, address 0x48
(if add0 is grounded) or 0x49 (if pulled up)
Outputs:
Analog RGB LED strip
I2C display(?)
Pump Activate/Deactivate (GPIO pin)
Some measurem... | normal | {
"blob_id": "06992263599fe3290c87ec00c6cb8af3748920c8",
"index": 5497,
"step-1": "\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# jan 2014 bbb garden shield attempt\n# AKA\n\n'''\nSensors:\nanalog level sensor, pin AIN0\nTMP102 i2c temperature sensor, address 0x48\n(if add0 is grounded) or 0x49 (if pulled up... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if year % 4 == 0 and year % 100 != 0:
print('閏年')
pass
elif year % 400 == 0:
print('閏年')
pass
else:
print('平年')
pass
<|reserved_special_token_1|>
year = int(input('西暦＞'))
if year % 4 == 0 and year % 100 ... | flexible | {
"blob_id": "b381d1110e6a7570cd872d689a43aba2d2580a23",
"index": 8449,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif year % 4 == 0 and year % 100 != 0:\n print('閏年')\n pass\nelif year % 400 == 0:\n print('閏年')\n pass\nelse:\n print('平年')\n pass\n",
"step-3": "year = int(input('西暦＞... | [
0,
1,
2
] |
#n = int(input())
#s = input()
n, m = map(int, input().split())
#s, t = input().split()
#n, m, l = map(int, input().split())
#s, t, r = input().split()
#a = map(int, input().split())
#a = input().split()
a = [int(input()) for _ in range(n)]
#a = [input() for _ in range(n)]
#t = input()
#m = int(input())
#p, q = map(in... | normal | {
"blob_id": "a09bc84a14718422894127a519d67dc0c6b13bc9",
"index": 746,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n - 1):\n if a[i + 1] - a[i] < m:\n ans += a[i + 1] - a[i]\n else:\n ans += m\nprint(ans)\n",
"step-3": "n, m = map(int, input().split())\na = [int(inp... | [
0,
1,
2,
3
] |
from functions2 import *
import numpy as np
#from functions import TermStructure,load_data
import numpy as np
import math
from scipy import optimize
import pylab as pl
from IPython import display as dp
class Vasicek():
def __init__(self,rs,vol):
self.t = rs.columns
self.ps= rs[-1:]
self.... | normal | {
"blob_id": "b6470ffda9040223951a99abc600ce1e99fe146b",
"index": 7902,
"step-1": "<mask token>\n\n\nclass Vasicek:\n\n def __init__(self, rs, vol):\n self.t = rs.columns\n self.ps = rs[-1:]\n self.sigma = vol\n <mask token>\n\n def loss(self, x):\n self.a = x[0]\n self... | [
3,
5,
6,
8,
9
] |
<|reserved_special_token_0|>
def multiprocessing_start(obj):
cov = init()
if cov:
multiprocessing.util.Finalize(None, multiprocessing_finish, args=(
cov,), exitpriority=1000)
<|reserved_special_token_0|>
def init():
cov_source = os.environ.get('COV_CORE_SOURCE')
cov_config = os... | flexible | {
"blob_id": "243794d36a1c6861c2c3308fe6a52ec19b73df72",
"index": 7820,
"step-1": "<mask token>\n\n\ndef multiprocessing_start(obj):\n cov = init()\n if cov:\n multiprocessing.util.Finalize(None, multiprocessing_finish, args=(\n cov,), exitpriority=1000)\n\n\n<mask token>\n\n\ndef init():\... | [
2,
3,
4,
5,
6
] |
import collections
import cPickle as pickle
import os
import shutil
import warnings
import numpy as np
import theano
import theano.tensor as T
import tables
#theano.config.compute_test_value = 'warn'
class SGD_Trainer(object):
"""Implementation of a stochastic gradient descent trainer
"""
#{{{ Properties
... | normal | {
"blob_id": "17ac827d181650cd8bd6e75ca7ff363d70d3c4a7",
"index": 2138,
"step-1": "import collections\nimport cPickle as pickle\nimport os\nimport shutil\nimport warnings\n\nimport numpy as np\nimport theano\nimport theano.tensor as T\nimport tables\n#theano.config.compute_test_value = 'warn'\n\n\nclass SGD_Train... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ClipboardEvent(Event):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ClipboardEvent(Event):
def __init__(self, text: str, *args, **kwargs):
super().__ini... | flexible | {
"blob_id": "9b02ce0b3acb14bdd6463c5bdba865b28253767c",
"index": 7896,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ClipboardEvent(Event):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass ClipboardEvent(Event):\n\n def __init__(self, text: str, *args, **kwargs):\n super().... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
monkey.patch_all()
<|reserved_special_token_0|>
http_server.serve_forever()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
monkey.patch_all()
<|reserved_special_token_0|>
http_server = WSGIServer(('0.0.0.0', 5000), a... | flexible | {
"blob_id": "c36625dfbd733767b09fcb5505d029ae2b16aa44",
"index": 7077,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmonkey.patch_all()\n<mask token>\nhttp_server.serve_forever()\n",
"step-3": "<mask token>\nmonkey.patch_all()\n<mask token>\nhttp_server = WSGIServer(('0.0.0.0', 5000), app)\nhttp_serve... | [
0,
1,
2,
3,
4
] |
# coding: utf-8
import pandas as pd
import os
import numpy as np
import json as json
import mysql.connector as sqlcnt
import datetime as dt
import requests
from mysql.connector.constants import SQLMode
import os
import glob
import re
import warnings
warnings.filterwarnings("ignore")
from pathlib import Path
# In[... | normal | {
"blob_id": "2060f57cfd910a308d60ad35ebbbf9ffd5678b9c",
"index": 3519,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwarnings.filterwarnings('ignore')\n<mask token>\nos.chdir(lib_path)\n<mask token>\nprint(res.summary())\n<mask token>\nX0\n<mask token>\nb\n<mask token>\ncovid_actual.loc[:, 'Date':'human... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
datacake_url = (
'https://api.datacake.co/integrations/api/ae6dd531-4cf6-4966-b5c9-6c43939aae90/'
)
serial = 'python0001'
number_of_persons_a = 234
number_of_persons_b = 3... | flexible | {
"blob_id": "00af9627242648a5a16a34a18bfc117945f1bc08",
"index": 4936,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n datacake_url = (\n 'https://api.datacake.co/integrations/api/ae6dd531-4cf6-4966-b5c9-6c43939aae90/'\n )\n serial = 'python0001'\n numbe... | [
0,
1,
2,
3
] |
#!/usr/bin/python
import os
from nao.tactics import Tactic
from nao.inspector import Inspector
def test_file():
print("\n[*] === file ===")
name_libmagic_so = 'libmagic.so.1'
inspector = Inspector("./sample/file", debug=True)
# find_addr = 0x1742D # ret block of is_tar
find_addr = 0x173F8 # return ... | normal | {
"blob_id": "a25fb9b59d86de5a3180e4257c4e398f22cdbb05",
"index": 6947,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_file():\n print('\\n[*] === file ===')\n name_libmagic_so = 'libmagic.so.1'\n inspector = Inspector('./sample/file', debug=True)\n find_addr = 95224\n cond = i... | [
0,
1,
2,
3,
4
] |
#embaralhar sorteio
import random
a1 = input('Primeiro aluno: ')
a2 = input('Primeiro segundo: ')
a3 = input('Primeiro terceiro: ')
a4 = input('Primeiro quarto: ')
lista = [a1, a2, a3, a4]
random.shuffle(lista)
print('A ordem de apresentacao será')
print(lista) | normal | {
"blob_id": "9a0e24fbe9f51dc914d891e90196c2ff4e65f04a",
"index": 9652,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nrandom.shuffle(lista)\nprint('A ordem de apresentacao será')\nprint(lista)\n",
"step-3": "<mask token>\na1 = input('Primeiro aluno: ')\na2 = input('Primeiro segundo: ')\na3 = input('Pri... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class ArchiveParserTest(unittest.TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def testReadFile(self):
"""Tests that file is read correctly.
Tests that correctly formatted file in archive is read correct... | flexible | {
"blob_id": "2ea335dd8d879731aad7713499440db6d1f60d36",
"index": 2427,
"step-1": "<mask token>\n\n\nclass ArchiveParserTest(unittest.TestCase):\n <mask token>\n <mask token>\n <mask token>\n\n def testReadFile(self):\n \"\"\"Tests that file is read correctly.\n\n Tests that correctly fo... | [
2,
3,
6,
7,
8
] |
from click.testing import CliRunner
from apitest.actions.cli import cli
def test_sendto_cli_runs_ok():
runner = CliRunner()
result = runner.invoke(cli, ["sendto"])
assert result.exit_code == 0
| normal | {
"blob_id": "7537deb4560e880365b23a99584d0b1f8fa3daf4",
"index": 5675,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_sendto_cli_runs_ok():\n runner = CliRunner()\n result = runner.invoke(cli, ['sendto'])\n assert result.exit_code == 0\n",
"step-3": "from click.testing import CliR... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
def GetDensity(T, P, config):
return P/(T*config["Flow"]["mixture"]["gasConstant"])
def GetViscosity(T, config):
if (config["Flow"]["mixture"]["viscosityModel"]["type"] == "Constant"):
viscosity = config["Flow"]["mixture"]["viscosityModel"]["Visc"]
elif (config["Flow"]["mixture"]... | normal | {
"blob_id": "0e47a7d9cd6809886674291d6a535dd18205a012",
"index": 5455,
"step-1": "<mask token>\n",
"step-2": "def GetDensity(T, P, config):\n return P / (T * config['Flow']['mixture']['gasConstant'])\n\n\n<mask token>\n",
"step-3": "def GetDensity(T, P, config):\n return P / (T * config['Flow']['mixtur... | [
0,
1,
2,
3
] |
from common.get_keyword import GetKeyword
from common.operation_Excel import OperationExcel
from common.op_database import OpDatabase
from interface.login import Login
from interface.address import Address
import unittest
import ddt
# 测试数据
op_excel = OperationExcel()
add_file = r'D:\pyCharm\Demo\pycode\Requests\201911... | normal | {
"blob_id": "0f0b3eea9dc397d32e81749304041abaf6651e94",
"index": 1873,
"step-1": "<mask token>\n\n\n@ddt.ddt\nclass TestAddress(unittest.TestCase):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def test_02_check_address(self):\n url = 'http://ecshop.itsoso.cn/ECMobile/?url... | [
2,
8,
9,
10,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def test_action_getter():
path = './../Version_1.0/Tests/General/Action_1.json'
document = json.loads(open(path).read())
gamestate = Gamestate.from_document(document['gamestate'])
nloops = 100
total_time = 0
... | flexible | {
"blob_id": "b16691429d83f6909a08b10cc0b310bb62cd550d",
"index": 3985,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_action_getter():\n path = './../Version_1.0/Tests/General/Action_1.json'\n document = json.loads(open(path).read())\n gamestate = Gamestate.from_document(document['g... | [
0,
1,
2,
3
] |
from datetime import datetime
from poop import objstore
class Comment(objstore.Item):
__typename__ = 'comment'
__table__ = 'comment'
relatesToId = objstore.column('relates_to_id')
relatesToVersion = objstore.column('relates_to_version')
posted = objstore.column()
approved = objstore.colum... | normal | {
"blob_id": "e398908ba74306c5a746d7643b38f08651cf92ec",
"index": 4205,
"step-1": "<mask token>\n\n\nclass Comment(objstore.Item):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, *a, **k):\n relatesTo = ... | [
2,
3,
4,
5,
6
] |
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