code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
# -*- coding: utf8 -*-
from django.db import models
import custom_fields
import datetime
#import mptt
# Create your models here.
class Message(models.Model):
user = models.ForeignKey('User')
time = models.DateTimeField(auto_now=True,auto_now_add=True)
text = models.TextField()
#true если это ответ подд... | normal | {
"blob_id": "64fd597918fe8133d53d1df741512cd2e49a111d",
"index": 1252,
"step-1": "<mask token>\n\n\nclass Ticket(models.Model):\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 token>\n ... | [
20,
22,
27,
29,
32
] |
# Copyright 2014 The Oppia Authors. All Rights Reserved.
#
# 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 ... | normal | {
"blob_id": "8a848eece6a3ed07889ba208068de4bfa0ad0bbf",
"index": 6744,
"step-1": "# Copyright 2014 The Oppia Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the Li... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in ip_net.hosts():
host_add = str(i)
toping = subprocess.Popen(['ping', '-n', '3', host_add], stdout=PIPE)
output = toping.communicate()[0]
hostalive = toping.returncode
if hostalive == 0:
print(h... | flexible | {
"blob_id": "414fb437783fcfb55f542f072aaf3a8bb02b441e",
"index": 8275,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in ip_net.hosts():\n host_add = str(i)\n toping = subprocess.Popen(['ping', '-n', '3', host_add], stdout=PIPE)\n output = toping.communicate()[0]\n hostalive = toping.re... | [
0,
1,
2,
3,
4
] |
import argparse
import pickle
import pandas as pd
from pyspark.sql.session import SparkSession
parser = argparse.ArgumentParser()
parser.add_argument('--rs', type=str, nargs='+')
args = parser.parse_args()
ss = SparkSession.builder.getOrCreate()
post_df = None
for f in args.rs:
df = ss.read.json(f).select('id', 'su... | normal | {
"blob_id": "e6b3def6ed6f2523d88912832a876caf2742b786",
"index": 7572,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('--rs', type=str, nargs='+')\n<mask token>\nfor f in args.rs:\n df = ss.read.json(f).select('id', 'subreddit', 'subreddit_id', 'title')\n post_df = df if post_df... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(1, 11):
if fave_fast_food in d:
d[fave_fast_food] += 1
else:
d[fave_fast_food] = 1
count += 1
fave_fast_food = input('Fave fast food restaurant: ')
for k, v in d.items():
print('F... | flexible | {
"blob_id": "a494b3469682a909b76e67e1b78ad25affe99f24",
"index": 8688,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, 11):\n if fave_fast_food in d:\n d[fave_fast_food] += 1\n else:\n d[fave_fast_food] = 1\n count += 1\n fave_fast_food = input('Fave fast food r... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_babi_en(get_10k=False):
data_dir = 'datasets/tasks_1-20_v1-2/en/'
if get_10k == True:
data_dir = 'datasets/tasks_1-20_v1-2/en-10k/'
maybe_download(
'https://s3.amazonaws.com/text-datasets/babi... | flexible | {
"blob_id": "7a4d04bd60b5f5555982af372145f9f4bcd83ca2",
"index": 8194,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_babi_en(get_10k=False):\n data_dir = 'datasets/tasks_1-20_v1-2/en/'\n if get_10k == True:\n data_dir = 'datasets/tasks_1-20_v1-2/en-10k/'\n maybe_download(\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def calcula_norma(x):
lista = []
for e in x:
lista.append(e ** 2)
v = sum(lista) ** (1 / 2)
return v
<|reserved_special_token_1|>
def calcula_norma(x):
lista=[]
for e in x:
lista.append(e**2)
v=(sum(lista)**(1/2)... | flexible | {
"blob_id": "7346992d69250240207a0fc981d0adc245e69f87",
"index": 5206,
"step-1": "<mask token>\n",
"step-2": "def calcula_norma(x):\n lista = []\n for e in x:\n lista.append(e ** 2)\n v = sum(lista) ** (1 / 2)\n return v\n",
"step-3": "def calcula_norma(x):\n lista=[]\n for e in x:\n... | [
0,
1,
2
] |
import keras
from keras.applications import VGG16
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
import matplotlib.pyplot as plt
from keras.callbacks import History
import numpy as np
import os
import cPickle as pickle
import scipy
from scipy import spatial
def getM... | normal | {
"blob_id": "461b2de86907047df53c3857c6b0397e77de3fcd",
"index": 5139,
"step-1": "import keras\r\nfrom keras.applications import VGG16\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\nfrom keras.models import Model\r\nimport matplotlib.pyplot as plt\r\nfrom keras.callbacks import History\r\nimport ... | [
0
] |
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.applications import InceptionV3
from keras.callbacks import ModelCheckpoint
from keras.optimizers import SGD
from keras.layers import Flatten,Dense,Dropout
from keras.preprocessing.image import img_to_a... | normal | {
"blob_id": "17a442a85b910ff47c2f3f01242b7f64a6237146",
"index": 9380,
"step-1": "from keras.preprocessing.image import ImageDataGenerator\r\nfrom keras.models import Sequential, Model\r\nfrom keras.applications import InceptionV3\r\nfrom keras.callbacks import ModelCheckpoint\r\nfrom keras.optimizers import SG... | [
0
] |
import torch
import numpy as np
import h5py
from torch.utils.data import Dataset, DataLoader
from config import PARAS
"""
Be careful:
We use log mel-spectrogram for training,
while the mask generated is for power mel-spectrogram
"""
def create_gt_mask(vocal_spec, bg_spec):
"""
Take in log spectrogram and ret... | normal | {
"blob_id": "1133d3cf900e31278dc491565c99969a116e6c83",
"index": 1998,
"step-1": "<mask token>\n\n\nclass TorchData(Dataset):\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_gt_mask(vocal_spec, bg_spec):\n \"\"\"\n Take in log spectrogra... | [
1,
6,
8,
9,
10
] |
class Meta(type):
def __new__(meta, name, bases, class_dict):
print(f'* Running {meta}.__new__ for {name}')
print("Bases:", bases)
print(class_dict)
return type.__new__(meta, name, bases, class_dict)
class MyClass(metaclass=Meta):
stuff = 123
def foo(self):
pass
cl... | normal | {
"blob_id": "8f3abc5beaded94b6d7b93ac2cfcd12145d75fe8",
"index": 522,
"step-1": "<mask token>\n\n\nclass MySubClass(MyClass):\n <mask token>\n <mask token>\n\n\n<mask token>\n\n\nclass MyClass2:\n stuff = 123\n\n def __init_subclass__(cls):\n super().__init_subclass__()\n print(f'* Runn... | [
8,
12,
14,
15,
17
] |
import os
os.mkdir("作业")
f=open("D:/six3/s/作业/tet.txt",'w+')
for i in range(10):
f.write("hello world\n")
f.seek(0)
s=f.read(100)
print(s)
f=open("D:/six3/s/作业/tet2.txt",'w+')
for i in s:
f.write(i)
f.close() | normal | {
"blob_id": "5f5e314d2d18deb12a8ae757a117ef8fbb2ddad5",
"index": 2391,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.mkdir('作业')\n<mask token>\nfor i in range(10):\n f.write('hello world\\n')\nf.seek(0)\n<mask token>\nprint(s)\n<mask token>\nfor i in s:\n f.write(i)\nf.close()\n",
"step-3": "... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open('datasetParsing2DEF.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
line_count = 0
for row in csv_reader:
if line_count == 0:
print(f"Column names are {', '.join(row)}"... | flexible | {
"blob_id": "084579152a2cc7feb2c31e0209ce1e32f4905d81",
"index": 5316,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('datasetParsing2DEF.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n for row in csv_reader:\n if line_count == 0:\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for card in cards:
try:
number = int(card)
if number % 2 == 0:
print(card, 'is an even card.')
except ValueError:
print(card, 'can not be divided')
<|reserved_special_token_1|>
cards ... | flexible | {
"blob_id": "b5180a2dbe1f12e1bbc92874c67ea99c9a84a9ed",
"index": 19,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor card in cards:\n try:\n number = int(card)\n if number % 2 == 0:\n print(card, 'is an even card.')\n except ValueError:\n print(card, 'can not be d... | [
0,
1,
2,
3
] |
INITIAL_B = 0.15062677711161448
B_FACTOR = 5.0
INITIAL_GE = 0.22581915788215678
GE_BOUNDS = [1.0 / 10.0, 1.0 / 4.0]
FIXED_P = 0.9401234488501574
INITIAL_GU = 0.2145066414796447
GU_BOUNDS = [1.0 / 15.0, 1.0 / 2.0]
INITIAL_GI = 0.19235137989123863
GI_BOUNDS = [1.0 / 15.0, 1.0 / 5.0]
INITIAL_GH = 0.044937075878220795... | normal | {
"blob_id": "47cf3045f2fa0f69759e09b1599e4afe953c06d8",
"index": 5138,
"step-1": "<mask token>\n",
"step-2": "INITIAL_B = 0.15062677711161448\nB_FACTOR = 5.0\nINITIAL_GE = 0.22581915788215678\nGE_BOUNDS = [1.0 / 10.0, 1.0 / 4.0]\nFIXED_P = 0.9401234488501574\nINITIAL_GU = 0.2145066414796447\nGU_BOUNDS = [1.0 /... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class MonitorList(tp.Generic[T], collections.UserList, Monitor):
<|reserved_special_token_0|>
def __init__(self, *args):
collections.UserList.__init__(self, *args)
Monitor.__init__(self)
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __... | flexible | {
"blob_id": "0528d7761cbbf3dbe881ff05b81060f3d97e7f6c",
"index": 742,
"step-1": "<mask token>\n\n\nclass MonitorList(tp.Generic[T], collections.UserList, Monitor):\n <mask token>\n\n def __init__(self, *args):\n collections.UserList.__init__(self, *args)\n Monitor.__init__(self)\n <mask to... | [
17,
20,
22,
23,
33
] |
ALPACA_KEY = 'Enter your apaca key here'
ALPACA_SECRET_KEY = 'Enter your apaca secret key here'
ALPACA_MARKET = 'enter alpaca market link here'
TWILIO_KEY = 'enter your twilio key here'
TWILIO_SECRET_KEY = 'enter your twilio secret key here'
YOUR_PHONE_NUMBER = 'Enter your phone number'
YOUR_TWILIO_NUMBER = 'Enter your... | normal | {
"blob_id": "10cb4b59d1e1e823c56ae5ceea0514b1c1904292",
"index": 3769,
"step-1": "<mask token>\n",
"step-2": "ALPACA_KEY = 'Enter your apaca key here'\nALPACA_SECRET_KEY = 'Enter your apaca secret key here'\nALPACA_MARKET = 'enter alpaca market link here'\nTWILIO_KEY = 'enter your twilio key here'\nTWILIO_SECR... | [
0,
1
] |
import os
import unittest
import json
from flask_sqlalchemy import SQLAlchemy
from app import create_app
from models import *
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv())
# auth tokens should be updated before running tests,
# make sure update the tokens in setup.sh
# read the README to kno... | normal | {
"blob_id": "bae4eb94d561f7aa810718840ff7c2de52cb0d6f",
"index": 3228,
"step-1": "<mask token>\n\n\nclass CastingAgencyTestCase(unittest.TestCase):\n <mask token>\n\n def setUp(self):\n \"\"\"Define test variables and initialize app.\"\"\"\n self.app = create_app()\n self.client = self... | [
21,
30,
31,
35,
39
] |
"""IDQ Importer Exporter
This script defines Import and Export functions through which it can communicate with
a Informatica Model Repository.
It also provides some related functions, such as:
- Create IDQ folder
- Check in IDQ components
Parts by Laurens Verhoeven
Parts by Jac. Beekers
@Version: 20190... | normal | {
"blob_id": "09b14705a6905470058b5eecc6dd0bb214975c66",
"index": 6408,
"step-1": "<mask token>\n\n\ndef import_infadeveloper(**KeyWordArguments):\n \"\"\"Import IDQ Components\"\"\"\n KeyWordArguments['Tool'] = 'Import'\n ImportCommand = buildCommand.build(**KeyWordArguments)\n result = executeInfacm... | [
10,
11,
12,
13,
14
] |
from django.shortcuts import render
from django_filters.rest_framework import DjangoFilterBackend
from django.views.decorators.csrf import csrf_exempt
from rest_framework.parsers import JSONParser
from django.http import JsonResponse, Http404
from .serializers import *
from .models import *
from .filter import *
from r... | normal | {
"blob_id": "e0c6fb414d87c0a6377538089226e37b044edc70",
"index": 8383,
"step-1": "<mask token>\n\n\n@csrf_exempt\ndef TBGRApi(request, tbgrno=0):\n if request.method == 'GET':\n tbgrs = TBGR.objects.all()\n tbgrs_serializer = TBGRSerializer(tbgrs, many=True)\n return JsonResponse(tbgrs_se... | [
3,
4,
5,
7,
8
] |
#!/usr/bin/env python
from __future__ import print_function, division, unicode_literals
import os
import sys
import json
import logging
import tempfile
import itertools
import traceback
import subprocess as sp
from os.path import basename
from datetime import datetime
from argparse import ArgumentParser, FileType
PRE... | normal | {
"blob_id": "ac19ae96d8262cadd43314c29198fccbc008c1b5",
"index": 6590,
"step-1": "#!/usr/bin/env python\n\nfrom __future__ import print_function, division, unicode_literals\nimport os\nimport sys\nimport json\nimport logging\nimport tempfile\nimport itertools\nimport traceback\nimport subprocess as sp\nfrom os.p... | [
0
] |
<|reserved_special_token_0|>
class World(pyglet.window.Window):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def setup(self):
self.width = 640
self.height = 480
self.rtri = 0.0
self.rquad = 0.0
... | flexible | {
"blob_id": "5fc097518b6069131e1ca58fa885c6ad45ae143c",
"index": 4741,
"step-1": "<mask token>\n\n\nclass World(pyglet.window.Window):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def setup(self):\n self.width = 640\n self.height = 480\n self.rtri = 0.0\n ... | [
9,
13,
15,
16,
17
] |
<|reserved_special_token_0|>
class User:
def __init__(self, name, id):
self.name = name
self.id = id
def __repr__(self):
return 'User({},{})'.format(self.name, self.id)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class User:
def ... | flexible | {
"blob_id": "e8ef3a5e41e68b4d219aa1403be392c51cc010e6",
"index": 7302,
"step-1": "<mask token>\n\n\nclass User:\n\n def __init__(self, name, id):\n self.name = name\n self.id = id\n\n def __repr__(self):\n return 'User({},{})'.format(self.name, self.id)\n\n\n<mask token>\n",
"step-2"... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open(filename, 'a') as handle:
handle.write(str(current_time))
handle.write('\n')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
filename = 'record_time.txt'
current_time = time.strftime('%a %H:%M:%S')
w... | flexible | {
"blob_id": "1f0695f0e9745912d8ee3a87e6c9b1272e9ebbae",
"index": 218,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(filename, 'a') as handle:\n handle.write(str(current_time))\n handle.write('\\n')\n",
"step-3": "<mask token>\nfilename = 'record_time.txt'\ncurrent_time = time.strftime(... | [
0,
1,
2,
3,
4
] |
import cv2 as cv
import numpy as np
from servo import *
from func import *
#import threading
#import dlib
# import socket
# import struct
# import pickle
def constrain(val, minv, maxv):
return min(maxv, max(minv, val))
KP = 0.22
KI = 0
KD = 0.17
last = 0
integral = 0
# constants
SIZE = (400, 300)
RECT = np.flo... | normal | {
"blob_id": "3ccbafbdc84447438c194288b1409e332bb2b479",
"index": 3630,
"step-1": "<mask token>\n\n\ndef constrain(val, minv, maxv):\n return min(maxv, max(minv, val))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef constrain(val, minv, maxv):\n return min(maxv, max(minv, val))\n\n\n<mask token>\nc... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def exportVSSD(path, camName, wantTris=False, renderdata=None):
mainFileDict = {}
mainFilePath = path
mainFileStem = os.path.basename(path)[:-5]
mainFileDir = os.path.dirname(path)
resolution = pmc.ls('defaul... | flexible | {
"blob_id": "004a9cd0e459116bf3f88f3546ff4eded3dfb2a8",
"index": 2512,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef exportVSSD(path, camName, wantTris=False, renderdata=None):\n mainFileDict = {}\n mainFilePath = path\n mainFileStem = os.path.basename(path)[:-5]\n mainFileDir = os.p... | [
0,
1,
2
] |
# len(): tamanho da string
# count(): conta quantas vezes um caractere aparece
# lower(), upper()
# replace(): substitui as letras por outra
# split(): quebra uma string a partir dos espacos em branco
a = len('Karen')
print(a)
b = 'Rainha Elizabeth'.count('a')
print(b)
c = 'karen nayara'.replace('a','@')
print(c)
d = ... | normal | {
"blob_id": "3079fdbe6319454ad166d06bda5670554a5746ee",
"index": 1004,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(a)\n<mask token>\nprint(b)\n<mask token>\nprint(c)\n<mask token>\nprint(d)\n",
"step-3": "a = len('Karen')\nprint(a)\nb = 'Rainha Elizabeth'.count('a')\nprint(b)\nc = 'karen nayar... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class TimeEntry:
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class TimeEntry:
def __init__(self, date, duration, togglproject='default toggl',
tdproject='default td', togglID='NULL', tdID='Null'):
self.duration = dura... | flexible | {
"blob_id": "bdf2c35c12820dd31bd242ce1b6dae7271ceb2b7",
"index": 8433,
"step-1": "<mask token>\n",
"step-2": "class TimeEntry:\n <mask token>\n",
"step-3": "class TimeEntry:\n\n def __init__(self, date, duration, togglproject='default toggl',\n tdproject='default td', togglID='NULL', tdID='Null'... | [
0,
1,
2
] |
<|reserved_special_token_0|>
@attr('unit')
class TestScp(unittest.TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
@patch('paramiko.SSHClient')
def test_scp_close(self, mock_connect):
self.dev.bind(scp=SCP)
self.dev.scp.open()
self.assertEqual(self.dev.scp.... | flexible | {
"blob_id": "65ea40ad1c1bf6bf23aed5316b91862c9cdc353d",
"index": 5564,
"step-1": "<mask token>\n\n\n@attr('unit')\nclass TestScp(unittest.TestCase):\n <mask token>\n <mask token>\n\n @patch('paramiko.SSHClient')\n def test_scp_close(self, mock_connect):\n self.dev.bind(scp=SCP)\n self.d... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('hello world')
print('welcome to london')
<|reserved_special_token_1|>
print("hello world")
print("welcome to london") | flexible | {
"blob_id": "cd322f9771f1ac90931a7229ffd5effd1cae1a54",
"index": 7207,
"step-1": "<mask token>\n",
"step-2": "print('hello world')\nprint('welcome to london')\n",
"step-3": "print(\"hello world\")\nprint(\"welcome to london\")",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
from load_blender_data import pose_spherical
from misc import mse, mse2psnr, to8b
import os
import imageio
import json
import torch
import torch.nn as nn
import numpy as np
import cv2
from torch.utils.data.dataset import Dataset
from torch.utils.data.dataloader import DataLoader
device = torch.device('cuda') if tor... | normal | {
"blob_id": "7180dc0d622fd449fcee32f2c50000d05ae2d8bb",
"index": 6850,
"step-1": "<mask token>\n\n\nclass BlenderDataset(Dataset):\n <mask token>\n <mask token>\n\n def get_coords2d(self, H, W):\n coord = np.linspace(0, 1, H, endpoint=False)\n coords = np.stack(np.meshgrid(coord, coord), -... | [
6,
9,
12,
14,
16
] |
def foo(x, y=5):
def bar(x):
return x + 1
return bar(y * 2)
print(foo(3))
| normal | {
"blob_id": "80d1979c5767d0ff90f464651c9d0ca6d65effb2",
"index": 6472,
"step-1": "<mask token>\n",
"step-2": "def foo(x, y=5):\n\n def bar(x):\n return x + 1\n return bar(y * 2)\n\n\n<mask token>\n",
"step-3": "def foo(x, y=5):\n\n def bar(x):\n return x + 1\n return bar(y * 2)\n\n\... | [
0,
1,
2
] |
<|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": "8ff7ace102b781b35fff0671e2c606bf662e2767",
"index": 9851,
"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 = [('education',... | [
0,
1,
2,
3,
4
] |
"""
# listbinmin.py
# Sam Connolly 04/03/2013
#===============================================================================
# bin data according a given column in an ascii file of column data, such that
# each bin has a minimum number of points, giving the bin of each data point as
# a LIST. UNEVEN BINS.
#========... | normal | {
"blob_id": "496c58e68d3ac78a3eb1272d61ca3603c5d843b6",
"index": 4787,
"step-1": "\"\"\"\n# listbinmin.py\n# Sam Connolly 04/03/2013\n\n#===============================================================================\n# bin data according a given column in an ascii file of column data, such that\n# each bin has ... | [
0
] |
<|reserved_special_token_0|>
class LRUCache:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def put(self, key, value):
if key in self.cache_map:
old_node = self.cache_map.get(key)
self.cache_list.remove(old_node)
new_node = Node(key, value)
... | flexible | {
"blob_id": "898ff6e38e80419d61ec4bbde827e8ca729eb19a",
"index": 5202,
"step-1": "<mask token>\n\n\nclass LRUCache:\n <mask token>\n <mask token>\n\n def put(self, key, value):\n if key in self.cache_map:\n old_node = self.cache_map.get(key)\n self.cache_list.remove(old_node... | [
2,
3,
4,
5
] |
import math
n, m, a = map(int, input().split())
top = math.ceil(n / a)
bottom = math.ceil(m / a)
print(top * bottom)
| normal | {
"blob_id": "6c426d2b165e01a7cec9f7ddbd96113ae05668f6",
"index": 4898,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(top * bottom)\n",
"step-3": "<mask token>\nn, m, a = map(int, input().split())\ntop = math.ceil(n / a)\nbottom = math.ceil(m / a)\nprint(top * bottom)\n",
"step-4": "import math... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
import sys
all_neighbors_coord = []
for i in range(-1, 2):
for j in range(-1, 2):
for k in range(-1, 2):
if i != 0 or j != 0 or k != 0:
all_neighbors_coord.append((i, j, k))
def add_coord(c1, c2):
return (c1[0] + c2[0], c1[1] + c2[1], c1[2] + c2[2])
... | normal | {
"blob_id": "e7060658ae1838b0870b2a3adb61c9f8d78c93c7",
"index": 3245,
"step-1": "<mask token>\n\n\nclass life:\n\n def __init__(self, world):\n self.world = world\n\n def get_world_size(self):\n xs = [c[0] for c in self.world]\n ys = [c[1] for c in self.world]\n zs = [c[2] for ... | [
10,
11,
12,
14,
16
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def validate_locale(locale: t.Union[Locale, str]) ->Locale:
if isinstance(locale, str):
try:
return Locale(locale)
except ValueError:
raise LocaleError(locale)
if not isinstance(lo... | flexible | {
"blob_id": "779445aa22145d5076940ea5b214c25ad233dd0e",
"index": 3087,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef validate_locale(locale: t.Union[Locale, str]) ->Locale:\n if isinstance(locale, str):\n try:\n return Locale(locale)\n except ValueError:\n ... | [
0,
1,
2,
3,
4
] |
from django import forms
from django.contrib.auth.models import User
from django.contrib.auth.forms import UserCreationForm
from .models import Upload
class DocumentForm(forms.ModelForm):
class Meta:
model = Upload
fields = ('document',)
| normal | {
"blob_id": "e7b1ccbcbb81ff02561d858a4db54d49a2aa0f8a",
"index": 6094,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass DocumentForm(forms.ModelForm):\n\n\n class Meta:\n model = Upload\n fields = 'document',\n",
"step-3": "from django import forms\nfrom django.contrib.auth.mod... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for episode in range(num_episodes):
state = env.reset()
done = False
rewards_current_episode = 0
for step in range(steps_per_episodes):
exploration_rate_threshold = random.uniform(0, 1)
if explorati... | flexible | {
"blob_id": "b791afec1c9fb214d1f3b4ec0ec67f905d96aabf",
"index": 3249,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor episode in range(num_episodes):\n state = env.reset()\n done = False\n rewards_current_episode = 0\n for step in range(steps_per_episodes):\n exploration_rate_thres... | [
0,
1,
2,
3,
4
] |
import urlparse
def parse_url(url):
"""
Parse a url into a ParseResult() object then evolve that ParseResult()
instance into an EasyUrl() object, finally return the EasyUrl() instance.
"""
url = urlparse.urlparse(url)
#print url.__class__
return EasyUrl.EvolveParseResult(url)
class ... | normal | {
"blob_id": "0d322bdaf1bfed2b76172cc4dfb1b9af52bdc641",
"index": 8264,
"step-1": "import urlparse\n\n\n\n\ndef parse_url(url):\n \"\"\" \n Parse a url into a ParseResult() object then evolve that ParseResult()\n instance into an EasyUrl() object, finally return the EasyUrl() instance.\n \"\"\"\n u... | [
0
] |
<|reserved_special_token_0|>
def sort_id(movies, titles):
ids = []
for i in titles:
try:
movie_id = MovieDB.objects.get(title=i).id
ids.append((i, movie_id))
except MovieDB.DoesNotExist:
return []
return ids
<|reserved_special_token_0|>
def sort_name... | flexible | {
"blob_id": "1e84b28580b97e77394be0490f3d8db3d62a2ccb",
"index": 1213,
"step-1": "<mask token>\n\n\ndef sort_id(movies, titles):\n ids = []\n for i in titles:\n try:\n movie_id = MovieDB.objects.get(title=i).id\n ids.append((i, movie_id))\n except MovieDB.DoesNotExist:\n... | [
3,
5,
6,
7,
8
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.28 on 2020-07-10 02:52
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('civictechprojects', '0036_auto_20200708_2251'),
]
operations = [
migration... | normal | {
"blob_id": "99154212d8d5fdb92cd972c727791158d09e3e2c",
"index": 3789,
"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 = [('civictechpr... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class CoursesDetailView(DetailView):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def get_context_data(self, *args, object_list=None, **kwargs):
context = super(CoursesDetailView, self).get_context_data(*args, **
kwargs)
print(context)... | flexible | {
"blob_id": "aaa9665ac6d639e681fddd032058f490ce36d12a",
"index": 7684,
"step-1": "<mask token>\n\n\nclass CoursesDetailView(DetailView):\n <mask token>\n <mask token>\n\n def get_context_data(self, *args, object_list=None, **kwargs):\n context = super(CoursesDetailView, self).get_context_data(*ar... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class DataGenerator(IterableDataset):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class CrossEncoderModel(torch.nn.Module):
def __init__(self):
super(CrossEncoderModel, self).__init__()
self.bert = AutoModel.from_pretrained('distilbert-base-cas... | flexible | {
"blob_id": "650f00dd9740d62546eb58724e6e5a74398b3e59",
"index": 2522,
"step-1": "<mask token>\n\n\nclass DataGenerator(IterableDataset):\n <mask token>\n <mask token>\n\n\nclass CrossEncoderModel(torch.nn.Module):\n\n def __init__(self):\n super(CrossEncoderModel, self).__init__()\n self.... | [
4,
7,
9,
10,
11
] |
import copy
import sys
import os
from datetime import datetime,timedelta
from dateutil.relativedelta import relativedelta
import numpy as np
import pandas
import tsprocClass as tc
import pestUtil as pu
#update parameter values and fixed/unfixed
#--since Joe is so pro-America...
tc.DATE_FMT = '%m/%d/%Y'
#--build ... | normal | {
"blob_id": "c060cdb7730ba5c4d2240b65331f5010cac222fa",
"index": 8721,
"step-1": "import copy\nimport sys\nimport os\nfrom datetime import datetime,timedelta\nfrom dateutil.relativedelta import relativedelta\nimport numpy as np\nimport pandas\n\nimport tsprocClass as tc \nimport pestUtil as pu \n\n#update param... | [
0
] |
''' Compress images '''
from PIL import Image
def resizeImage(image_file):
try:
# get the image's width and height in pixels
img = Image.open(image_file)
width, height = img.size
# get the largest dimension
max_dim = max(img.size)
if max_dim > 1000:
# resize the image using the largest side as dime... | normal | {
"blob_id": "1b43125c2ebffd0a268a4a0ffdbbf407de7b0374",
"index": 7486,
"step-1": "''' Compress images '''\n\nfrom PIL import Image\n\n\ndef resizeImage(image_file):\n\ttry:\n\t\t# get the image's width and height in pixels\n\t\timg = Image.open(image_file)\n\t\twidth, height = img.size\n\n\t\t# get the largest d... | [
0
] |
<|reserved_special_token_0|>
class TelaLisatrClientes:
<|reserved_special_token_0|>
def init_components(self, lista_clientes):
layout = [[sg.Text('Dados do cliente')], [sg.Listbox(values=
lista_clientes, size=(60, 10))], [sg.Submit()]]
self.__window = sg.Window('Lista de clientes'... | flexible | {
"blob_id": "624b34d160ea6db4f5249544f1614a20f506ca9e",
"index": 895,
"step-1": "<mask token>\n\n\nclass TelaLisatrClientes:\n <mask token>\n\n def init_components(self, lista_clientes):\n layout = [[sg.Text('Dados do cliente')], [sg.Listbox(values=\n lista_clientes, size=(60, 10))], [sg.... | [
2,
3,
4,
5,
6
] |
def read_int():
return int(input().strip())
def read_ints():
return list(map(int, input().strip().split(' ')))
def solve():
K, S = read_ints()
# X+Y+Z = S
# 0 <= X,Y,Z <= K
total = 0
for X in range(K+1):
if S-X < 0:
break
# Y+Z=S-X
Y_min = max(S-X-K,... | normal | {
"blob_id": "46b1fc975fbeedcafaa66c85c378e2249a495647",
"index": 8827,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef read_ints():\n return list(map(int, input().strip().split(' ')))\n\n\ndef solve():\n K, S = read_ints()\n total = 0\n for X in range(K + 1):\n if S - X < 0:\n ... | [
0,
2,
3,
4,
5
] |
# USAGE
# python predict_video.py --model model/activity.model --label-bin model/lb.pickle --input example_clips/lifting.mp4 --output output/lifting_128avg.avi --size 128
# python predict_video.py --model model/road_activity.model --label-bin model/rd.pickle --input example_clips/fire_footage.mp4 --ou
# tput output/fir... | normal | {
"blob_id": "ccfcc5b644d592090786ceb35a85124c9d3275ad",
"index": 5719,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef index():\n return render_template('Main_page.html')\n\n\n@app.route('/prediction.html')\ndef predict():\n return render_template('prediction.html')\n\n\n@app.route('/About_us.html')\ndef... | [
4,
5,
6,
7,
8
] |
import itertools
import unittest
from pylev3 import Levenshtein
TEST_DATA = [
('classic', "kitten", "sitting", 3),
('same', "kitten", "kitten", 0),
('empty', "", "", 0),
('a', "meilenstein", "levenshtein", 4),
('b', "levenshtein", "frankenstein", 6),
('c', "confide", "deceit", 6),
('d', "... | normal | {
"blob_id": "892d6662e4276f96797c9654d15c96a608d0835a",
"index": 8927,
"step-1": "<mask token>\n\n\nclass Tests(unittest.TestCase):\n\n def test_singleton(self):\n lev1, lev2 = Levenshtein(), Levenshtein()\n self.assertIs(lev1, lev2)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Tes... | [
2,
3,
4,
5,
7
] |
import torch.nn as nn
from torch.autograd import Variable
import torch
import string
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
#Find letter index from all_letters, e.g. "a" = 0
def letterToIndex(letter):
return all_letters.find(letter)
#Only for demonstation
def letterToTensor(let... | normal | {
"blob_id": "aa24442624aebeb2777f16a826cf59859d7870ba",
"index": 8744,
"step-1": "<mask token>\n\n\ndef letterToIndex(letter):\n return all_letters.find(letter)\n\n\n<mask token>\n\n\nclass RNN(nn.Module):\n\n def __init__(self, input_size, hidden_size, output_size):\n super(RNN, self).__init__()\n ... | [
6,
7,
10,
11,
13
] |
<|reserved_special_token_0|>
def get_changelog():
with open(os.path.join(here, 'CHANGELOG'), encoding='utf-8') as f:
text = f.read()
header_matches = list(re.finditer('^=+$', text, re.MULTILINE))
text = text[:header_matches[5].start()]
lines = text.splitlines()[:-1]
return '=========\nChan... | flexible | {
"blob_id": "c81889cf4d87933b562aa4618bc5185a8d213107",
"index": 8075,
"step-1": "<mask token>\n\n\ndef get_changelog():\n with open(os.path.join(here, 'CHANGELOG'), encoding='utf-8') as f:\n text = f.read()\n header_matches = list(re.finditer('^=+$', text, re.MULTILINE))\n text = text[:header_ma... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
plt.imshow(img, cmap='gray', interpolation='bicubic')
plt.show()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
img = cv2.imread('test.jpg', cv2.IMREAD_... | flexible | {
"blob_id": "34ccaaf5eb47afd556588cd94cddbddaee1f0b53",
"index": 2851,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.imshow('image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\nplt.imshow(img, cmap='gray', interpolation='bicubic')\nplt.show()\n",
"step-3": "<mask token>\nimg = cv2.imread('test.... | [
0,
1,
2,
3,
4
] |
import unittest
import TicTacToe
class pVpTestCase(unittest.TestCase):
# def test_something(self):
# self.assertEqual(True, False)
def twoplayer_setup(self):
game1 = TicTacToe.Game()
player1 = TicTacToe.Player('X', game1)
player2 = TicTacToe.Player('O', game1)
return (g... | normal | {
"blob_id": "de0521db3909054c333ac3877ff0adf15ab180fb",
"index": 1732,
"step-1": "<mask token>\n\n\nclass CvPTestCase(unittest.TestCase):\n\n def onecompplayer_setup(self):\n game1 = TicTacToe.Game()\n computer1 = TicTacToe.Computer('X', game1)\n player2 = TicTacToe.Player('O', game1)\n ... | [
13,
15,
17,
20,
22
] |
#!/usr/bin/env python3
# coding=utf-8
import fire
import json
import os
import time
import requests
import time
import hashlib
import random
root_path, file_name = os.path.split(os.path.realpath(__file__))
ip_list_path = ''.join([root_path, os.path.sep, 'ip_list.json'])
class ProxySwift(object):
... | normal | {
"blob_id": "0ff96b2314927d7b3e763242e554fd561f3c9343",
"index": 5872,
"step-1": "<mask token>\n\n\nclass ProxySwift(object):\n <mask token>\n\n def requerst_get(self, url, data, *p, **kwargs):\n SecretKey = '3JCx8fAF7Bpq5Aj4t9wS7cfVB7hpXZ7j'\n PartnerID = '2017061217350058'\n TimeStam... | [
9,
10,
13,
14,
16
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
model.fit(data_train, label_train)
<|reserved_special_token_0|>
print(model.score(data_test, label_test))
print(accuracy_score(label_test, predictions))
print(accuracy_score(label_test, predictions, normalize=False))
print(metrics... | flexible | {
"blob_id": "33365d5ce5d2a7d28b76a7897de25e1f35d28855",
"index": 6269,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmodel.fit(data_train, label_train)\n<mask token>\nprint(model.score(data_test, label_test))\nprint(accuracy_score(label_test, predictions))\nprint(accuracy_score(label_test, predictions, ... | [
0,
1,
2,
3,
4
] |
from tornado import gen
import rethinkdb as r
from .connection import connection
from .utils import dump_cursor
@gen.coroutine
def get_promotion_keys():
conn = yield connection()
result = yield r.table('promotion_keys').run(conn)
result = yield dump_cursor(result)
return result
@gen.coroutine
def p... | normal | {
"blob_id": "66cdfdfa797c9991e5cb169c4b94a1e7041ca458",
"index": 4772,
"step-1": "<mask token>\n\n\n@gen.coroutine\ndef pop_promotion_key(promotion_key):\n conn = yield connection()\n result = yield r.table('promotion_keys').get(promotion_key).delete(\n return_changes=True).run(conn)\n if result[... | [
1,
2,
3,
4,
5
] |
# put your python code here
a = int(input())
b = int(input())
# and
i = 1
if a == b:
print(a)
else:
while True:
if i // a > 0 and i % a == 0 and i // b > 0 and i % b == 0:
print(i)
break
else:
i += 1
| normal | {
"blob_id": "af5ebdcd818fdf9c607240733b7b5dbb793cf55e",
"index": 7328,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif a == b:\n print(a)\nelse:\n while True:\n if i // a > 0 and i % a == 0 and i // b > 0 and i % b == 0:\n print(i)\n break\n else:\n ... | [
0,
1,
2,
3
] |
import pytest
from components import models
pytestmark = pytest.mark.django_db
def test_app_models():
assert models.ComponentsApp.allowed_subpage_models() == [
models.ComponentsApp,
models.BannerComponent,
]
def test_app_required_translatable_fields():
assert models.ComponentsApp.get_r... | normal | {
"blob_id": "b1622aa65422fcb69a16ad48a26fd9ed05b10382",
"index": 8882,
"step-1": "<mask token>\n\n\ndef test_app_models():\n assert models.ComponentsApp.allowed_subpage_models() == [models.\n ComponentsApp, models.BannerComponent]\n\n\n<mask token>\n\n\n@pytest.mark.django_db\ndef test_set_slug(en_loca... | [
2,
3,
4,
5,
6
] |
# (1) Obtain your values here (https://core.telegram.org/api/obtaining_api_id)
api_id = 000000
api_hash = '00000000000000000000000'
phone = '+000000000000'
username = 'theone'
project_id = 000000000
| normal | {
"blob_id": "a5646a5d42dbf6e70e9d18f28513ee2df68a28b1",
"index": 6886,
"step-1": "<mask token>\n",
"step-2": "api_id = 0\napi_hash = '00000000000000000000000'\nphone = '+000000000000'\nusername = 'theone'\nproject_id = 0\n",
"step-3": "# (1) Obtain your values here (https://core.telegram.org/api/obtaining_ap... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(numbers)
print(numbers[1])
print(numbers[-1])
<|reserved_special_token_0|>
print(numbers)
del numbers[1]
print(numbers)
numbers.append(17)
print(numbers)
numbers.insert(2, 5)
print(numbers)
numbers.sort()
print(numbers)
<|... | flexible | {
"blob_id": "34d3eebf6ccb19f891ccbb16db47cd6412f1cb0f",
"index": 1155,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(numbers)\nprint(numbers[1])\nprint(numbers[-1])\n<mask token>\nprint(numbers)\ndel numbers[1]\nprint(numbers)\nnumbers.append(17)\nprint(numbers)\nnumbers.insert(2, 5)\nprint(number... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
"""
Update the expected test outputs and inputs for rsmsummarize and rsmcompare tests.
This script assumes that you have already run `nose2 -s tests` and ran the entire
test suite. By doing so, the output has been generated under the given outputs
directory. And that is what will be used to gener... | normal | {
"blob_id": "7e20c61fa30ea93e69a2479e70449638eb52b7bb",
"index": 2964,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n parser = argparse.ArgumentParser(prog='update_test_files.py')\n parser.add_argument('--tests', dest='tests_dir', required=True, help=\n 'The path to the exi... | [
0,
1,
2,
3,
4
] |
from __future__ import print_function, absolute_import, division
import os
import h5py
import glob
import copy
import numpy as np
from tqdm import tqdm
# from utils.pose import draw_skeleton
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import poseutils.camera_utils as cameras
from pose... | normal | {
"blob_id": "cf6dffb28e37003212d3e3402dee58a57a7d9869",
"index": 5192,
"step-1": "<mask token>\n\n\nclass TDPWDataset(object):\n\n def __init__(self, path, center_2d=False, load_metrics=None, skel_norm=\n False):\n super(TDPWDataset, self).__init__()\n self.cameras = None\n self._d... | [
5,
8,
10,
11,
15
] |
# -*- coding: utf-8 -*-
class Task:
def __init__(self):
self.title = ''
self.subtasks = []
def set_title(self, title):
self.title = title
def set_subtasks(self, subtasks):
self.subtasks = subtasks
| normal | {
"blob_id": "3cf2ffbc8163c2a447016c93ff4dd13e410fff2b",
"index": 7353,
"step-1": "<mask token>\n",
"step-2": "class Task:\n <mask token>\n <mask token>\n\n def set_subtasks(self, subtasks):\n self.subtasks = subtasks\n",
"step-3": "class Task:\n\n def __init__(self):\n self.title = ... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def largo(l, n):
i = 0
cuenta = 1
valor1 = 0
valor2 = 0
while cuenta < n + 1 or cuenta == n + 1:
a = l[i]
b = l[i + 1]
if a == b:
cuenta += 1
valor1 = a
... | flexible | {
"blob_id": "f3b697e20f60e51d80d655ddf4809aa9afdfcd69",
"index": 7495,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef largo(l, n):\n i = 0\n cuenta = 1\n valor1 = 0\n valor2 = 0\n while cuenta < n + 1 or cuenta == n + 1:\n a = l[i]\n b = l[i + 1]\n if a == b:\n... | [
0,
1,
2,
3,
4
] |
# Generated by Django 3.1 on 2020-08-28 14:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('api_rest', '0004_auto_20200828_0749'),
]
operations = [
migrations.RemoveField(
model_name='event',
name='user_id',
... | normal | {
"blob_id": "bfd8385e8f4886b91dde59c04785134b9cd6a2b6",
"index": 3893,
"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 = [('api_rest', ... | [
0,
1,
2,
3,
4
] |
from .base import * # noqa
from .base import env
# exemple https://github.com/pydanny/cookiecutter-django/blob/master/%7B%7Bcookiecutter.project_slug%7D%7D/config/settings/production.py
# GENERAL
# ------------------------------------------------------------------------------
# https://docs.djangoproject.com/en/dev/r... | normal | {
"blob_id": "836df02495ee581f138050be6b7a7a076ea899eb",
"index": 4966,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nSECRET_KEY = env('DJANGO_SECRET_KEY')\nALLOWED_HOSTS = [x.split(':') for x in env.list('DJANGO_ALLOWED_HOSTS')]\nADMINS = [x.split(':') for x in env.list('DJANGO_ADMINS')]\nDATABASES['def... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class TestPanel(wx.Panel):
def __init__(self, parent, log):
self.log = log
wx.Panel.__init__(self, parent, -1)
b1 = wx.Button(self, -1, 'Create and Show a MiniFrame', (50, 50))
self.Bind(wx.EVT_BUTTON, self.OnButton1, b1)
b2 = wx.Button(self, -... | flexible | {
"blob_id": "b041e9577af72d2bcee3dda0cc78fa12800d53bd",
"index": 2286,
"step-1": "<mask token>\n\n\nclass TestPanel(wx.Panel):\n\n def __init__(self, parent, log):\n self.log = log\n wx.Panel.__init__(self, parent, -1)\n b1 = wx.Button(self, -1, 'Create and Show a MiniFrame', (50, 50))\n ... | [
12,
14,
16,
19,
22
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class CurriculoSerializer(serializers.ModelSerializer):
class Meta:
model = Curriculo
fields = 'id', 'name', 'description', 'image', 'create_at', 'update_at'
<|reserved_special_token_1|>
from rest_framew... | flexible | {
"blob_id": "029f4f015f558dbd4d6096b00c53f5f0fe69883d",
"index": 1322,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass CurriculoSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Curriculo\n fields = 'id', 'name', 'description', 'image', 'create_at', 'update_at... | [
0,
1,
2,
3
] |
# Import smtplib for the actual sending function
import smtplib
# Import the email modules we'll need
from email.message import EmailMessage
# Open the plain text file whose name is in textfile for reading.
with open("testfile.txt") as fp:
# Create a text/plain message
msg = EmailMessage()
msg.set_content... | normal | {
"blob_id": "9feb24da78113310509664fa9efcf5f399be5335",
"index": 5914,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('testfile.txt') as fp:\n msg = EmailMessage()\n msg.set_content('test')\n<mask token>\ns.send_message(msg)\ns.quit()\n",
"step-3": "<mask token>\nwith open('testfile.txt... | [
0,
1,
2,
3,
4
] |
"""
This file goes through the data to find the frequencies of words in the corpus
"""
import csv
import time, datetime
import calendar
from collections import defaultdict
import chardet
import re
REVIEW_ID_COL = 0;
USER_ID_COL = 1
BUSINESS_ID_COL = 2
STARS_COL = 3
DATE_COL = 4
TEXT_COL = 5
USEFUL_CO... | normal | {
"blob_id": "ba54b3a148a34ced74a337665ddd5f2d9084553b",
"index": 1489,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('yelp_review.csv', encoding='utf8') as csvfile:\n wordFrequencies = defaultdict(int)\n\n def beautifyDate(res):\n dt = time.strptime(res, '%Y-%m-%d')\n retur... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
brick.sound.beep()
wait(1000)
motor_a.run_target(500, 720)
wait(1000)
brick.sound.beep(1000, 500)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
motor_a = Motor(Port.A)
brick.sound.beep()
wait(1000)
motor_a.run_targe... | flexible | {
"blob_id": "f6ebc3c37a69e5ec49d91609db394eec4a94cedf",
"index": 9982,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nbrick.sound.beep()\nwait(1000)\nmotor_a.run_target(500, 720)\nwait(1000)\nbrick.sound.beep(1000, 500)\n",
"step-3": "<mask token>\nmotor_a = Motor(Port.A)\nbrick.sound.beep()\nwait(1000... | [
0,
1,
2,
3,
4
] |
# drop data to file filter
import tarr.compiler_base
def format_data(data):
return '{0.id}: {0.payload}'.format(data)
class WRITE_TO_FILE(tarr.compiler_base.Instruction):
@property
def __name__(self):
return 'POINT OF INTEREST - WRITE("{}")'.format(self.filename)
def __init__(self, filenam... | normal | {
"blob_id": "75393d39b147097a7ac1d82938ac102491ea9441",
"index": 8469,
"step-1": "<mask token>\n\n\nclass WRITE_TO_FILE(tarr.compiler_base.Instruction):\n\n @property\n def __name__(self):\n return 'POINT OF INTEREST - WRITE(\"{}\")'.format(self.filename)\n\n def __init__(self, filename, formatte... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def draw_chat(id, smooth_id, main_mode, my_name, chat_day_data, main_plot,
pie_plot, list_chats_plot):
min_in_day = 1440
possible_smooth = [1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24,
30, 32, 36, 40,... | flexible | {
"blob_id": "b297a09ee19bb8069eb65eb085903b3219c6fe5a",
"index": 7971,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef draw_chat(id, smooth_id, main_mode, my_name, chat_day_data, main_plot,\n pie_plot, list_chats_plot):\n min_in_day = 1440\n possible_smooth = [1, 2, 3, 4, 5, 6, 8, 9, 10, ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open('./roc.txt', 'r') as fin:
with open('./roc_shuffle.txt', 'w') as fout:
tmp = []
for k, line in enumerate(fin):
i = k + 1
if i % 6 == 0:
idx = [0] + np.random.pe... | flexible | {
"blob_id": "2aec0581413d4fb0ffb4090231fde0fed974bf18",
"index": 27,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('./roc.txt', 'r') as fin:\n with open('./roc_shuffle.txt', 'w') as fout:\n tmp = []\n for k, line in enumerate(fin):\n i = k + 1\n if i % 6 ... | [
0,
1,
2,
3
] |
import os
error_msg = '''The default transformer cannot handle slashes (subdirectories);
try another transformer in vlermv.transformers.'''
def to_path(key):
if isinstance(key, tuple):
if len(key) == 1:
key = key[0]
else:
raise ValueError(error_msg)
if '/' in key or '\... | normal | {
"blob_id": "e4ff6d689a7da5b16786fd59d6a4707b9b6e3e7d",
"index": 8076,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef to_path(key):\n if isinstance(key, tuple):\n if len(key) == 1:\n key = key[0]\n else:\n raise ValueError(error_msg)\n if '/' in key or '\... | [
0,
2,
3,
4,
5
] |
from selenium import webdriver
import time
def test_check_error_page_1():
try:
link = "http://suninjuly.github.io/registration1.html"
browser = webdriver.Chrome()
browser.get(link)
# Проверяем Fisrt name*
field_text = browser.find_element_by_xpath(
'//body/div/f... | normal | {
"blob_id": "83ebebbb6191295adcb58b003bf1c3bcc6fb189f",
"index": 7405,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_check_error_page_1():\n try:\n link = 'http://suninjuly.github.io/registration1.html'\n browser = webdriver.Chrome()\n browser.get(link)\n fiel... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def greedy(s, k):
m_1 = random.choice(list(s.keys()))
medoids = {m_1: s[m_1]}
dimensions = list(range(len(s[m_1])))
s.pop(m_1)
dist = {}
for x in s:
dist[x] = Manhattan_segmental_dist.manhattan_se... | flexible | {
"blob_id": "9a02bd0bc14494db033c032003aa5baea111ea8c",
"index": 7185,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef greedy(s, k):\n m_1 = random.choice(list(s.keys()))\n medoids = {m_1: s[m_1]}\n dimensions = list(range(len(s[m_1])))\n s.pop(m_1)\n dist = {}\n for x in s:\n ... | [
0,
1,
2,
3
] |
import tensorflow as tf
import bbox_lib
def hard_negative_loss_mining(c_loss, negative_mask, k):
"""Hard negative mining in classification loss."""
# make sure at least one negative example
k = tf.maximum(k, 1)
# make sure at most all negative.
k = tf.minimum(k, c_loss.shape[-1])
neg_c_loss = ... | normal | {
"blob_id": "6e17fef4507c72190a77976e4a8b2f56880f2d6f",
"index": 4895,
"step-1": "<mask token>\n\n\ndef hard_negative_loss_mining(c_loss, negative_mask, k):\n \"\"\"Hard negative mining in classification loss.\"\"\"\n k = tf.maximum(k, 1)\n k = tf.minimum(k, c_loss.shape[-1])\n neg_c_loss = c_loss * ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class SpiderForCROWDCUBETest(unittest.TestCase):
def setUp(self):
logging.basicConfig(level=logging.INFO)
self.spider = SpiderForCROWDCUBE()
self.spider.initDriver()
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def test_downloadCompan... | flexible | {
"blob_id": "45856b4c5cbf1d3b414ad769135b2d974bc0a22b",
"index": 7120,
"step-1": "<mask token>\n\n\nclass SpiderForCROWDCUBETest(unittest.TestCase):\n\n def setUp(self):\n logging.basicConfig(level=logging.INFO)\n self.spider = SpiderForCROWDCUBE()\n self.spider.initDriver()\n <mask to... | [
3,
4,
6,
7,
8
] |
<|reserved_special_token_0|>
class Script(BaseScript):
<|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_special_token_0|>... | flexible | {
"blob_id": "40b3c403f99044eb61740d62eda15ddd08b0f739",
"index": 1980,
"step-1": "<mask token>\n\n\nclass Script(BaseScript):\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 token>\n <m... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
explore_patterns = [url('^$', views.explore), url('^(?P<model_type>\\w+)/$',
views.get_by_model_type), url('^(?P<model_type>\\w+)/(?P<id>\\w+)/$',
views.get_by_model_id), url(
'^(?P<model_type>\\w+)/(?P<id>\\w+)/downlo... | flexible | {
"blob_id": "89078ddd7dad3a2727b66566457b9ac173abe607",
"index": 8506,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nexplore_patterns = [url('^$', views.explore), url('^(?P<model_type>\\\\w+)/$',\n views.get_by_model_type), url('^(?P<model_type>\\\\w+)/(?P<id>\\\\w+)/$',\n views.get_by_model_id), ... | [
0,
1,
2,
3
] |
import torch
import random
from itertools import product
from Struct import Action
class Agent(object):
"""the agent"""
def __init__(self, q, epsilon=0.8, discount=0.9, learningRate=0.5, traceDecay=0.3):
# action set
possibleChangesPerMagnet = (1e-2, 1e-3, 0, -1e-2, -1e-3)
# possible... | normal | {
"blob_id": "63edbbbad9561ddae005d2b5e22a089819dc34c5",
"index": 1821,
"step-1": "<mask token>\n\n\nclass Agent(object):\n <mask token>\n\n def __init__(self, q, epsilon=0.8, discount=0.9, learningRate=0.5,\n traceDecay=0.3):\n possibleChangesPerMagnet = 0.01, 0.001, 0, -0.01, -0.001\n ... | [
8,
9,
10,
12,
13
] |
__version__ = '0.2.11'
# This list defines all the modules that will be loaded if a user invokes
# from climLab import *
# totally out of date!
#__all__ = ["constants", "thermo", "orbital_table",
# "long_orbital_table", "insolation", "ebm",
# "column", "convadj"]
#from climlab import radiatio... | normal | {
"blob_id": "8251a9c798b3cdc2f374d0a0406ccfaa11b7c5e3",
"index": 5699,
"step-1": "<mask token>\n",
"step-2": "__version__ = '0.2.11'\n<mask token>\n",
"step-3": "__version__ = '0.2.11'\nfrom climlab.utils import constants\nfrom climlab.utils import thermo, legendre\nfrom climlab.model.column import GreyRadia... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Application(tk.Frame):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def show_win(self):
msg = 'YOU WIN!'
mb.showinfo('Information', msg)
self.makePlayButtons()
def move(self, num):
def move2(self=self, num=num):
... | flexible | {
"blob_id": "f29bc0263f8bb1d59ab2442347727d9d3233ec77",
"index": 9893,
"step-1": "<mask token>\n\n\nclass Application(tk.Frame):\n <mask token>\n <mask token>\n\n def show_win(self):\n msg = 'YOU WIN!'\n mb.showinfo('Information', msg)\n self.makePlayButtons()\n\n def move(self, ... | [
5,
7,
8,
9,
11
] |
<|reserved_special_token_0|>
def download_install_deb(package_path, package_url):
download_file(package_path, package_url)
install_debian_package_binary(package_path)
remove_file(package_path)
<|reserved_special_token_0|>
def write_file(path, data, mode='w'):
if os.path.exists(path) and mode is no... | flexible | {
"blob_id": "f546eb40ee8a7308ded62532731561029e5ec335",
"index": 7870,
"step-1": "<mask token>\n\n\ndef download_install_deb(package_path, package_url):\n download_file(package_path, package_url)\n install_debian_package_binary(package_path)\n remove_file(package_path)\n\n\n<mask token>\n\n\ndef write_f... | [
4,
6,
7,
8,
10
] |
<|reserved_special_token_0|>
class MainHandler(BaseHandler):
<|reserved_special_token_0|>
def get(self):
"""Returns the root endpoint of the API."""
self.write(
'{"error": "cryptochat-server main page, please refer to /api/message/new or /api/message/updates"}'
)
cla... | flexible | {
"blob_id": "9f8d79d141d414c1256e39f58e59f97711acfee4",
"index": 4915,
"step-1": "<mask token>\n\n\nclass MainHandler(BaseHandler):\n <mask token>\n\n def get(self):\n \"\"\"Returns the root endpoint of the API.\"\"\"\n self.write(\n '{\"error\": \"cryptochat-server main page, plea... | [
17,
19,
22,
25,
31
] |
# -*- coding: utf-8 -*-
"""Code handling the concurrency of data analysis."""
| normal | {
"blob_id": "2e23225ec4cd693f5e9460a13d64206f184a86a0",
"index": 3043,
"step-1": "<mask token>\n",
"step-2": "# -*- coding: utf-8 -*-\n\"\"\"Code handling the concurrency of data analysis.\"\"\"\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
<|reserved_special_token_0|>
def forward(self):
z = self.beta[:self.dim]
r1_local = self.beta[self.dim:2 * self.dim]
r2_local = self.beta[2 * self.dim:3 * se... | flexible | {
"blob_id": "27e9e63338d422b5fca6f7a67fa3d255602a3358",
"index": 225,
"step-1": "<mask token>\n\n\nclass V_test_abstract(V):\n\n def __init__(self):\n super(V_test_abstract, self).__init__()\n <mask token>\n\n def forward(self):\n z = self.beta[:self.dim]\n r1_local = self.beta[self... | [
3,
4,
5,
6,
7
] |
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY ... | normal | {
"blob_id": "a83230e71cc1bcc843d00487746f16114d304eec",
"index": 4908,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef mge_to_caffe(mge_fpath, prototxt='out.prototxt', caffemodel=\n 'out.caffemodel', outspec=None, use_empty_blobs=False):\n assert isinstance(mge_fpath, str), 'mge_fpath must b... | [
0,
1,
2,
3
] |
#!/home/liud/anaconda3/envs/python/bin/python
# -*- coding: utf-8 -*-
'''
线性回归
公式：W = 1/(xTx) * xT * y
'''
#导入的包
import numpy as np
from numpy import linalg
from numpy import corrcoef
from sklearn import linear_model
import matplotlib.pyplot as plt
#加载数据
def loadDataSet(filename):
xList = []
yList = []
with open(... | normal | {
"blob_id": "a6eab1e5e7985de917d707c904fcd90f223c108c",
"index": 2559,
"step-1": "#!/home/liud/anaconda3/envs/python/bin/python\n# -*- coding: utf-8 -*-\n'''\n\t线性回归\n\t公式：W = 1/(xTx) * xT * y\n'''\n#导入的包\nimport numpy as np\nfrom numpy import linalg\nfrom numpy import corrcoef\nfrom sklearn import linear_model\... | [
0
] |
import spacy
from vaderSentiment import vaderSentiment
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/')
def hello():
return render_template('index.html')
@app.route('/',methods=['POST'])
def func():
st=request.form["review"]
if(st==''):
return render_temp... | normal | {
"blob_id": "2d7f7cb66480ecb8335949687854554679026959",
"index": 9988,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef hello():\n return render_template('index.html')\n\n\n@app.route('/', methods=['POST'])\ndef func():\n st = request.form['review']\n if st == '':\n return render_template('index... | [
4,
5,
6,
7,
8
] |
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 31 05:48:57 2019
@author: emama
"""
import datetime as dt
t = dt.datetime.today()
print(t) | normal | {
"blob_id": "b1fbc8f3616b70e5d35898fd895c37e838c87dc9",
"index": 9293,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(t)\n",
"step-3": "<mask token>\nt = dt.datetime.today()\nprint(t)\n",
"step-4": "<mask token>\nimport datetime as dt\nt = dt.datetime.today()\nprint(t)\n",
"step-5": "# -*- co... | [
0,
1,
2,
3,
4
] |
print("hello world")
print("lol")
print("new changes in vis") | normal | {
"blob_id": "6c88e55a76cbd84cee0ebd6c51d930cc2da100d2",
"index": 2945,
"step-1": "<mask token>\n",
"step-2": "print('hello world')\nprint('lol')\nprint('new changes in vis')\n",
"step-3": "print(\"hello world\")\nprint(\"lol\")\nprint(\"new changes in vis\")",
"step-4": null,
"step-5": null,
"step-ids"... | [
0,
1,
2
] |
import pytest
from apistar import App, Route, TestClient, exceptions
from apistar_request_id import RequestId, RequestIdHooks
def index() -> dict:
return {}
def fail() -> dict:
raise exceptions.BadRequest("fail")
def fail_2() -> dict:
raise RuntimeError("fail")
routes = [
Route("/", method="GET... | normal | {
"blob_id": "f41ab6813fb7067089abe223b9006adde40630cd",
"index": 1941,
"step-1": "<mask token>\n\n\ndef index() ->dict:\n return {}\n\n\n<mask token>\n\n\n@pytest.fixture\ndef client(app):\n return TestClient(app)\n\n\ndef test_request_id_can_be_autogenerated(client):\n response = client.get('/')\n a... | [
6,
9,
10,
11,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
s.connect((HOST, PORT))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
HOST = '127.0.0.1'
PORT = 4444
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((HOST, PORT))
<|reserved_special_token_1|>
impo... | flexible | {
"blob_id": "14a39b9aa56777c8198794fe2f51c9a068500743",
"index": 4075,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ns.connect((HOST, PORT))\n",
"step-3": "<mask token>\nHOST = '127.0.0.1'\nPORT = 4444\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.connect((HOST, PORT))\n",
"step-4": "imp... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TGAbstractRegistry(ABC):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TGAbstractRegistry(ABC):
def __init__(self):
self.rule_engine = TGLoggingRuleEngin... | flexible | {
"blob_id": "d499b4e189a0c3c6efa6a07871dbc6c2996a2dcb",
"index": 2245,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TGAbstractRegistry(ABC):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TGAbstractRegistry(ABC):\n\n def __init__(self):\n self.rule_engine = TGLoggingRule... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
admin.site.register(Post)
<|reserved_special_token_1|>
from django.contrib import admin
from trips.models import Post
admin.site.register(Post)
| flexible | {
"blob_id": "a8197a4f0bb84e734696bf43fa976c76732d75b8",
"index": 9863,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(Post)\n",
"step-3": "from django.contrib import admin\nfrom trips.models import Post\nadmin.site.register(Post)\n",
"step-4": null,
"step-5": null,
"step-ids":... | [
0,
1,
2
] |
config_info = {'n_input': 1, 'num_layers': 1, 'features': 20,
'sequence_length': 1344, 'num_steps': None, 'lstm_size': None,
'batch_size': None, 'init_learning_rate': None, 'learning_rate_decay':
None, 'init_epoch': None, 'max_epoch': None, 'dropout_rate': None}
| normal | {
"blob_id": "8ede786526f4b730173777d9d3b9c7e4554fc887",
"index": 2443,
"step-1": "<mask token>\n",
"step-2": "config_info = {'n_input': 1, 'num_layers': 1, 'features': 20,\n 'sequence_length': 1344, 'num_steps': None, 'lstm_size': None,\n 'batch_size': None, 'init_learning_rate': None, 'learning_rate_dec... | [
0,
1
] |
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