code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
from django.db import models
from django.utils import timezone
from accounts.models import AllUser
from profiles.models import Profile
### MODEL HOLDING MEMBER TO CLIENT RELATIONSHIPS. ###
class MemberClient(models.Model):
created = models.DateTimeField(auto_now_add=timezone.now())
client = models.ForeignKey(... | normal | {
"blob_id": "b419e26cbf5bbb746f897367ddaa829773a6860c",
"index": 7742,
"step-1": "<mask token>\n\n\nclass MemberClient(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass MemberClient(models.Model):\n <mask token>\n ... | [
1,
2,
3,
4,
5
] |
import numpy
#Matrixmultiplikation
#Matrixinvertierung
#nicht p inv
#selbst invertierbar machen
import math
import operator | normal | {
"blob_id": "ece20c8c8fae2225cbac3552e254314b7116057c",
"index": 7095,
"step-1": "<mask token>\n",
"step-2": "import numpy\nimport math\nimport operator\n",
"step-3": "import numpy\n#Matrixmultiplikation\n#Matrixinvertierung\n#nicht p inv\n#selbst invertierbar machen\n\nimport math\nimport operator",
"step... | [
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": "1c1cd0eeea4dbf446aa4582f42ef1f3b5a4e8875",
"index": 7452,
"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 = [('meeting', '... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@register.filter
def td_humanize(diff):
if diff.total_seconds() < 0:
return 'Meni jo!'
days = diff.days
if days >= 7:
weeks, days = divmod(days, 7)
result = str(weeks) + ' vk'
if days:
result += ' ' + str(days) + ' pv'
return... | flexible | {
"blob_id": "43792a647243b9d667d6d98b62a086d742e8e910",
"index": 6093,
"step-1": "<mask token>\n\n\n@register.filter\ndef td_humanize(diff):\n if diff.total_seconds() < 0:\n return 'Meni jo!'\n days = diff.days\n if days >= 7:\n weeks, days = divmod(days, 7)\n result = str(weeks) + ... | [
2,
7,
8,
9,
12
] |
# defining private variables
class Privacy:
def __init__(self, val):
self.__val = 900;
print("Private data member =",self.__val,"\n")
value = Privacy(800);
print("Value not changable\n")
value.__val;
| normal | {
"blob_id": "b767519229058b50183d78bb97121f050e5b6bad",
"index": 423,
"step-1": "class Privacy:\n <mask token>\n\n\n<mask token>\n",
"step-2": "class Privacy:\n\n def __init__(self, val):\n self.__val = 900\n print('Private data member =', self.__val, '\\n')\n\n\n<mask token>\n",
"step-3"... | [
1,
2,
3,
4,
5
] |
def ispalindrome(s):
if len(s) <= 1:
return True
elif s[0] != s[-1]:
return False
else:
return ispalindrome(s[1:-1])
| normal | {
"blob_id": "c20a414f7f96a96f6e458fc27e5d2c7ac7ab05cf",
"index": 8574,
"step-1": "<mask token>\n",
"step-2": "def ispalindrome(s):\n if len(s) <= 1:\n return True\n elif s[0] != s[-1]:\n return False\n else:\n return ispalindrome(s[1:-1])\n",
"step-3": null,
"step-4": null,
... | [
0,
1
] |
<|reserved_special_token_0|>
def patternToNumber(pattern):
if len(pattern) == 0:
return 0
return 4 * patternToNumber(pattern[0:-1]) + symbolToNumber(pattern[-1:])
def symbolToNumber(symbol):
if symbol == 'A':
return 0
if symbol == 'C':
return 1
if symbol == 'G':
r... | flexible | {
"blob_id": "51848a64102f7fe8272fcf56a9792ed50c430538",
"index": 9115,
"step-1": "<mask token>\n\n\ndef patternToNumber(pattern):\n if len(pattern) == 0:\n return 0\n return 4 * patternToNumber(pattern[0:-1]) + symbolToNumber(pattern[-1:])\n\n\ndef symbolToNumber(symbol):\n if symbol == 'A':\n ... | [
8,
9,
11,
13,
15
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.append(os.pardir)
<|reserved_special_token_0|>
for key in optimizers.keys():
networks[key] = MultiLayerNet(input_size=784, hidden_size_list=[100,
100, 100, 100], output_size=10)
train_loss[key] = []
for i... | flexible | {
"blob_id": "85d40a49341c7bd7af7a5dc62e4bce0253eb25e6",
"index": 9944,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append(os.pardir)\n<mask token>\nfor key in optimizers.keys():\n networks[key] = MultiLayerNet(input_size=784, hidden_size_list=[100, \n 100, 100, 100], output_size=10)... | [
0,
1,
2,
3,
4
] |
from models import Ban
from django.shortcuts import render_to_response
class IPBanMiddleware(object):
"""
Simple middleware for taking care of bans from specific IP's
Redirects the banned user to a ban-page with an explanation
"""
def process_request(self, request):
ip = request.META['REMOTE_ADDR'] # use... | normal | {
"blob_id": "9289eb32db145187c5b4140e32acff520be8366e",
"index": 7620,
"step-1": "<mask token>\n\n\nclass IPBanMiddleware(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass IPBanMiddleware(object):\n <mask token>\n\n def process_request(self, request):\n ip = reque... | [
1,
2,
3,
4,
5
] |
import ray
import os
import sys
import random
path_join = os.path.join
real_path = os.path.realpath
perfd_dir = real_path(path_join(os.getcwd()))
microps_dir = path_join(perfd_dir, "thirdparty", "microps")
sys.path += [perfd_dir, microps_dir]
from thirdparty.microps.oracle.experiments.spark_sql_perf.main import Spar... | normal | {
"blob_id": "25595b5f86a41fee1dc43f199f3bcff73f6d256b",
"index": 9418,
"step-1": "<mask token>\n\n\n@ray.remote\ndef run(run_config: dict, wrks: dict) ->dict:\n try:\n add_spk_role()\n except:\n print('run, spark: ignore')\n os.chdir(microps_dir)\n base_spk_config = spk.apps_config_map[... | [
1,
2,
3,
4,
5
] |
<|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": "8b0eed6d1f24b5dd30726ce08c97354a5d5ab69b",
"index": 7597,
"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 = [('grafit', '0... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def main():
if len(sys.argv) != 3:
sys.stderr.write('USAGE: %s input output\n' % sys.argv[0])
sys.stderr.flush()
sys.exit(0)
with open(sys.argv[1]) as inpt, open(sys.argv[2], 'w') as outp:
process(inpt, outp)
<|reserved_special_token_0|>
<|reser... | flexible | {
"blob_id": "f819d1b1f2f6f3052247cda592007eac40aca37a",
"index": 7927,
"step-1": "<mask token>\n\n\ndef main():\n if len(sys.argv) != 3:\n sys.stderr.write('USAGE: %s input output\\n' % sys.argv[0])\n sys.stderr.flush()\n sys.exit(0)\n with open(sys.argv[1]) as inpt, open(sys.argv[2], ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class StardogGraphStore(GraphStore):
<|reserved_special_token_0|>
def check_whether_db_exists(self):
logger.debug("Checking whether a triple store with db '{}' exists..."
.format(self._node_ts_url))
url = self._get_ts_db_url()
r = requests.get(... | flexible | {
"blob_id": "a42a94798d176e20646d41cf0f4b7e4f99e0790b",
"index": 105,
"step-1": "<mask token>\n\n\nclass StardogGraphStore(GraphStore):\n <mask token>\n\n def check_whether_db_exists(self):\n logger.debug(\"Checking whether a triple store with db '{}' exists...\"\n .format(self._node_ts_u... | [
4,
5,
6,
7,
8
] |
from kivy.app import App
from kivy.uix.floatlayout import FloatLayout
class LayoutWindow(FloatLayout):
pass
class floatlayoutApp(App):
def build(self):
return LayoutWindow()
if __name__== "__main__":
display = floatlayoutApp()
display.run() | normal | {
"blob_id": "2af8677e76b77b9bfa579012a85ea331c0c7f390",
"index": 136,
"step-1": "<mask token>\n\n\nclass floatlayoutApp(App):\n\n def build(self):\n return LayoutWindow()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass LayoutWindow(FloatLayout):\n pass\n\n\nclass floatlayoutApp(App):\n\n ... | [
2,
3,
4,
5,
6
] |
class TflearnDataSourceExtraTemplate(object):
"""
Base class for TFLearn's DataSource (if we use wrapping).
Parameters:
----------
rewrite_data_aug : bool
use wrapper for data augmentation
"""
def __init__(self, rewrite_data_aug=False):
self.rewrite_data_aug = rewrite_data_... | normal | {
"blob_id": "70c084dab8469ca34b0e3e5174101111e695f1ca",
"index": 6638,
"step-1": "<mask token>\n",
"step-2": "class TflearnDataSourceExtraTemplate(object):\n <mask token>\n <mask token>\n",
"step-3": "class TflearnDataSourceExtraTemplate(object):\n <mask token>\n\n def __init__(self, rewrite_data... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def downgrade():
op.drop_column('stakeholder', 'archived')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def upgrade():
op.add_column('stakeholder', sa.Column('archived', sa.Boolean(),
nullable=False, default=False, server_default='false'))
def downgrade... | flexible | {
"blob_id": "42d9f40dd50056b1c258508a6cb3f9875680276a",
"index": 3393,
"step-1": "<mask token>\n\n\ndef downgrade():\n op.drop_column('stakeholder', 'archived')\n",
"step-2": "<mask token>\n\n\ndef upgrade():\n op.add_column('stakeholder', sa.Column('archived', sa.Boolean(),\n nullable=False, defa... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author : 河北雪域网络科技有限公司 A.Star
# @contact: astar@snowland.ltd
# @site:
# @file: img_to_sketch.py
# @time: 2018/8/6 1:15
# @Software: PyCharm
from skimage.color import rgb2grey
import numpy as np
def sketch(img, threshold=15):
"""
素描画生成
param img: Image实例
... | normal | {
"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
] |
/Users/tanzy/anaconda3/lib/python3.6/_dummy_thread.py | normal | {
"blob_id": "08a5a903d3757f8821554aa3649ec2ac2b2995a5",
"index": 911,
"step-1": "/Users/tanzy/anaconda3/lib/python3.6/_dummy_thread.py",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
from math import ceil, log2, sqrt
def constructST(s, start, end, st, i):
if start == end:
st[i] = 0
openst[i] = 1 if s[start] == '(' else 0
closedst[i] = 1 if s[start] == ')' else 0
return st[i], openst[i], closedst[i]
else:
mid = (start+end)//2
st[i], openst[i], closedst[i] = constructST(s, ... | normal | {
"blob_id": "ccc74f58eff3bb00f0be8c2c963de4208b7f0933",
"index": 9125,
"step-1": "<mask token>\n\n\ndef constructST(s, start, end, st, i):\n if start == end:\n st[i] = 0\n openst[i] = 1 if s[start] == '(' else 0\n closedst[i] = 1 if s[start] == ')' else 0\n return st[i], openst[i],... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if recipe not in recipes:
user.add_recipes([recipe])
db.session.commit()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
user = User.query.filter_by(username='xiaofan').first()
recipe = Recipe.query.filter_by(... | flexible | {
"blob_id": "07f8fd305e2311c0e37a785da0a826b8ea4e78ba",
"index": 4154,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif recipe not in recipes:\n user.add_recipes([recipe])\n db.session.commit()\n",
"step-3": "<mask token>\nuser = User.query.filter_by(username='xiaofan').first()\nrecipe = Recipe.... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def get_basename(name, split_num):
return f'{name}.split{split_num:d}'
<|reserved_special_token_0|>
def maybe_load_model(name, split_num, checkpoint_dir, resume_from_epoch,
batch_norm, l1_factor, l2_factor, optimizer):
"""
Attempt to load the specified model (including... | flexible | {
"blob_id": "6553312c9655c821444ff5f60e4d68c7fc08bd08",
"index": 1118,
"step-1": "<mask token>\n\n\ndef get_basename(name, split_num):\n return f'{name}.split{split_num:d}'\n\n\n<mask token>\n\n\ndef maybe_load_model(name, split_num, checkpoint_dir, resume_from_epoch,\n batch_norm, l1_factor, l2_factor, op... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class DensePoseConfig(ZambaBaseModel):
<|reserved_special_token_0|>
video_loader_config: VideoLoaderConfig
output_type: DensePoseOutputEnum
render_output: bool = False
embeddings_in_json: bool = False
data_dir: Path
filepaths: Optional[Path] = None
save_dir... | flexible | {
"blob_id": "9d8d8e97f7d3dbbb47dc6d4105f0f1ffb358fd2f",
"index": 6977,
"step-1": "<mask token>\n\n\nclass DensePoseConfig(ZambaBaseModel):\n <mask token>\n video_loader_config: VideoLoaderConfig\n output_type: DensePoseOutputEnum\n render_output: bool = False\n embeddings_in_json: bool = False\n ... | [
4,
5,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def tetrahedron_filled(tetrahedrons, water):
var = 0
br = 0
tetrahedrons.sort()
for numbers in tetrahedrons:
v = tetrahedrons[var] ** 3 * 2 ** 0.5 / 12000
if v < water:
br = br + 1
water = water - v
... | flexible | {
"blob_id": "c926e16ef2daa5978b6c71e7794721d320bb9b1e",
"index": 1224,
"step-1": "<mask token>\n",
"step-2": "def tetrahedron_filled(tetrahedrons, water):\n var = 0\n br = 0\n tetrahedrons.sort()\n for numbers in tetrahedrons:\n v = tetrahedrons[var] ** 3 * 2 ** 0.5 / 12000\n if v < w... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class InvoiceServiceTestCase(TestCase):
<|reserved_special_token_0|>
def test_create_invoice(self):
invoice = self.invoice_service.create_invoice(amount=12.1, status=
InvoiceStatusChoices.OVERDUE, due_date=date(2019, 4, 1), debtor
=self.debtor_1)
... | flexible | {
"blob_id": "5f77e93d63c696363c30f019019acd22c694308b",
"index": 4529,
"step-1": "<mask token>\n\n\nclass InvoiceServiceTestCase(TestCase):\n <mask token>\n\n def test_create_invoice(self):\n invoice = self.invoice_service.create_invoice(amount=12.1, status=\n InvoiceStatusChoices.OVERDUE... | [
3,
4,
5,
6
] |
#!/usr/bin/env python
__author__ = "Maxime Beauchamp"
__version__ = "0.1"
__date__ = "2020-12-10"
__email__ = "maxime.beauchamp@imt-atantique.fr"
from graphics_OSSE import *
# function to create recursive paths
def mk_dir_recursive(dir_path):
if os.path.isdir(dir_path):
return
h, t = os.path.split(di... | normal | {
"blob_id": "9f4cd9ed8aea03f5908aef4a154d964f0810619b",
"index": 9820,
"step-1": "<mask token>\n\n\ndef mk_dir_recursive(dir_path):\n if os.path.isdir(dir_path):\n return\n h, t = os.path.split(dir_path)\n if not os.path.isdir(h):\n mk_dir_recursive(h)\n new_path = join_paths(h, t)\n ... | [
1,
2,
3,
4,
5
] |
from django.http import HttpResponse
from polls.models import Pregunta
from django.template import loader
def index(request):
preguntas = Pregunta.objects.order_by('-fecha')[:5]
template = loader.get_template('polls/index.html')
context = { 'listado': preguntas,}
return HttpResponse(template.render(c... | normal | {
"blob_id": "07dc058ecef323ffd41299245e4fcafdc9e41506",
"index": 2131,
"step-1": "<mask token>\n\n\ndef resultados(request, total):\n latest_question_list = Pregunta.objects.order_by('fecha')[:total]\n output = ', '.join([q.descripcion for q in latest_question_list])\n return HttpResponse(output)\n\n\n<... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def f(x, y):
return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def f(x, y):
return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)
<|reserved_special_token_0|>
ax... | flexible | {
"blob_id": "e9c439eafac8fd689980ffcb562f3b5ee903dd56",
"index": 2604,
"step-1": "<mask token>\n\n\ndef f(x, y):\n return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef f(x, y):\n return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y **... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Actor:
def __init__(self):
self.x = random.random() * sizex
self.y = random.random() * sizey
self.xn = self.x
self.yn = self.y
def step(self):
t = getnoise(self.x, self.y) * 5 * math.pi
self.x = self.xn
self.y = self.... | flexible | {
"blob_id": "68c9944c788b9976660384e5d1cd0a736c4cd0e6",
"index": 3826,
"step-1": "<mask token>\n\n\nclass Actor:\n\n def __init__(self):\n self.x = random.random() * sizex\n self.y = random.random() * sizey\n self.xn = self.x\n self.yn = self.y\n\n def step(self):\n t = g... | [
3,
4,
6,
7
] |
<|reserved_special_token_0|>
class TestComputeReverseDependencies(unittest.TestCase):
def setUp(self):
repo_0 = Repository(packages_from_definition(PACKAGE_DEF_0))
repo_1 = Repository(packages_from_definition(PACKAGE_DEF_1))
self.repos = [repo_0, repo_1]
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "fcf19c49bb161305eaa5ba8bc26e276a8e8db8ea",
"index": 3925,
"step-1": "<mask token>\n\n\nclass TestComputeReverseDependencies(unittest.TestCase):\n\n def setUp(self):\n repo_0 = Repository(packages_from_definition(PACKAGE_DEF_0))\n repo_1 = Repository(packages_from_definition(PACKAGE_... | [
5,
12,
13,
14,
18
] |
# 1.- Crear una grafica que muestre la desviacion tipica de los datos cada dia para todos los pacientes
# 2.- Crear una grafica que muestre a la vez la inflamacion maxima, media y minima para cada dia
import numpy as np
data = np.loadtxt(fname='inflammation-01.csv', delimiter=',')
import matplotlib.pyplot as pl... | normal | {
"blob_id": "52064b518ad067c9906e7de8542d9a399076a0b5",
"index": 4214,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.plot(data.std(axis=0))\nplt.show()\nplt.plot(data.max(axis=0))\nplt.plot(data.mean(axis=0))\nplt.plot(data.min(axis=0))\n",
"step-3": "<mask token>\ndata = np.loadtxt(fname='inflamm... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class BaseCard(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __getattr__(self, item):
"""
添加魔术方法
:param item:
:return:
"""
operation = it... | flexible | {
"blob_id": "93e5852df00733c024a59d37699bae58bd893030",
"index": 112,
"step-1": "<mask token>\n\n\nclass BaseCard(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __getattr__(self, item):\n \"\"\"\n 添加魔术方法\n :param item:\n :return:\n \... | [
2,
3,
4,
7,
9
] |
class Solution:
def minimumDeletions(self, nums: List[int]) ->int:
n = len(nums)
a = nums.index(min(nums))
b = nums.index(max(nums))
if a > b:
a, b = b, a
return min(a + 1 + n - b, b + 1, n - a)
| normal | {
"blob_id": "14f3c941856ddf6bd7b3e046f21072f0b5f7b036",
"index": 5009,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def minimumDeletions(self, nums: List[int]) ->int:\n n = len(nums)\n a = nums.index(min(nums))\n b = nums.... | [
0,
1,
2
] |
# Copyright (c) 2023 Intel Corporation
# 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 law or agreed to in writ... | normal | {
"blob_id": "cd1ada2d7979fffc17f707ed113efde7aa134954",
"index": 3036,
"step-1": "<mask token>\n\n\n@api(canonical_alias='nncf.torch.create_compressed_model')\n@tracked_function(NNCF_PT_CATEGORY, [CompressionStartedFromConfig(argname=\n 'config')])\ndef create_compressed_model(model: Module, config: NNCFConfi... | [
2,
3,
5,
6,
7
] |
n = int(input())
p = [220000] + list(map(int, input().split()))
cnt = 0
m = 220000
for i in range(1, n + 1):
now = p[i]
m = min(m, now)
if now == m:
cnt += 1
print(cnt)
| normal | {
"blob_id": "2a500968cf6786440c0d4240430433db90d1fc2f",
"index": 5941,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, n + 1):\n now = p[i]\n m = min(m, now)\n if now == m:\n cnt += 1\nprint(cnt)\n",
"step-3": "n = int(input())\np = [220000] + list(map(int, input().spli... | [
0,
1,
2
] |
from itertools import groupby
def solve(tribes):
attacks = []
for t in tribes:
D, N, W, E, S, DD, DP, DS = t
for i in range(N):
d = D + DD * i
w = W + DP * i
e = E + DP * i
s = S + DS * i
attacks.append((d, w, e, s))
attacks = sort... | normal | {
"blob_id": "362bfc5a35b09817ce071e71a72e574a28ea287d",
"index": 3365,
"step-1": "<mask token>\n\n\ndef line(f):\n return map(int, f.readline().split())\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef solve(tribes):\n attacks = []\n for t in tribes:\n D, N, W, E, S, DD, DP, DS = t\n ... | [
1,
3,
4,
5,
6
] |
from . import resources
from jsonschema import validate
from jsonschema.exceptions import ValidationError
import aiohttp_client
import importlib.resources as pkg_resources
import json
import logging
log = logging.getLogger("amplitude-client")
API_URL = "https://api.amplitude.com/2/httpapi"
class AmplitudeLogger:
... | normal | {
"blob_id": "d32f009f373249b7b602ac36f29982273a2ed192",
"index": 2289,
"step-1": "<mask token>\n\n\nclass AmplitudeLogger:\n <mask token>\n\n async def log_event(self, event):\n event = {'api_key': self.api_key, 'events': [event]}\n try:\n validate(instance=event, schema=self.api_s... | [
1,
2,
3,
4,
5
] |
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import math
from tkinter import *
from tkinter.ttk import *
from facedetectandtrack import *
x_vals = []
root = Tk()
counter=0
#def graph():
plt.style.use('seaborn')
def animate(i):
data = pd.read_csv('data.csv... | normal | {
"blob_id": "239f055fd76a3ecb5f384c256ad850ea42739b8f",
"index": 9710,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.style.use('seaborn')\n\n\ndef animate(i):\n data = pd.read_csv('data.csv')\n global x_vals\n global counter\n x_vals.append(counter)\n try:\n x = data.iloc[x_val... | [
0,
2,
3,
4,
5
] |
a=10
b=20
c=400
d=100
e=500
f=30
z=a+b+c+d+e+f
print "The total sum is",z
print "variable d added"
print "Variable e added"
print "Variable f is equal to 30"
print "You are coming from test branch"
print "Your are very new in this branch"
| normal | {
"blob_id": "700d876dd45548b74b563ed86f8124fa666e1739",
"index": 2588,
"step-1": "a=10\nb=20\nc=400\nd=100\ne=500\nf=30\nz=a+b+c+d+e+f\nprint \"The total sum is\",z\nprint \"variable d added\"\nprint \"Variable e added\"\nprint \"Variable f is equal to 30\"\nprint \"You are coming from test branch\"\nprint \"You... | [
0
] |
from QnA_processor.question_analysis.google_question_classifier import GoogleQuestionClassifier
def classify_question(query):
try:
"""
Get answer-type from google autoML classifier
(by making POST requests with authorization key)
"""
question_c... | normal | {
"blob_id": "db231ea92319414dd10ca8dfbc14e5a70ed2fe44",
"index": 7343,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef classify_question(query):\n try:\n \"\"\"\n Get answer-type from google autoML classifier \n (by making POST requests with authorization key)\n \"\"... | [
0,
1,
2,
3
] |
import ssl
import urllib
from urllib import request, response, error, parse, robotparser
context = ssl._create_unverified_context()
url = 'https://oauth.51job.com/get_login.php?client_id=000001&redirect_uri=https%3A%2F%2Funion.yingjiesheng.com%2Fapi_login.php&from_domain=yjs_web&display=default&state=7c893ec1be7b355a91... | normal | {
"blob_id": "2a37d02c7a0840e855a80adced4794fd757e353a",
"index": 2917,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(res.read().decode('utf-8'))\n",
"step-3": "<mask token>\ncontext = ssl._create_unverified_context()\nurl = (\n 'https://oauth.51job.com/get_login.php?client_id=000001&redirect_... | [
0,
1,
2,
3,
4
] |
# coding=utf-8
# Copyright 2022 The TensorFlow Datasets Authors.
#
# 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 appl... | normal | {
"blob_id": "ed65d7e0de3fc792753e34b77254bccc8cee6d66",
"index": 3657,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_data_dir_register():\n register = register_path.DataDirRegister(namespace_to_data_dirs={'ns1':\n [epath.Path('/path/ns1')]})\n assert {'ns1'} == register.namespa... | [
0,
1,
2,
3
] |
from django.apps import AppConfig
class WebApiAppConfig(AppConfig):
name = 'WebApiApp'
| normal | {
"blob_id": "cc97f70b9d41357f020ea9c59d8b149392a336cc",
"index": 9656,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass WebApiAppConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass WebApiAppConfig(AppConfig):\n name = 'WebApiApp'\n",
"step-4": "from django.apps impo... | [
0,
1,
2,
3
] |
import os, sys
sys.path.append('./Pytorch-UNet/')
import torch
from torch import optim
import torchvision.transforms as transforms
import torchvision.datasets as dset
import wandb
from datasets import parse_dataset_args, create_dataset
from wt_utils import wt, create_filters, load_checkpoint, load_weights
from argumen... | normal | {
"blob_id": "fbd5c7fa335d6bde112e41a55d15aee31e3ebaf7",
"index": 2759,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append('./Pytorch-UNet/')\n<mask token>\nif __name__ == '__main__':\n logger = Logger()\n torch.backends.cudnn.benchmark = True\n args = parse_args()\n logger.update_... | [
0,
1,
2,
3
] |
import cv2
import sys
import online as API
def demo(myAPI):
myAPI.setAttr()
video_capture = cv2.VideoCapture(0)
print("Press q to quit: ")
while True:
# Capture frame-by-frame
ret, frame = video_capture.read() #np.array
frame = cv2.resize(frame, (320, 240))
key = cv2.w... | normal | {
"blob_id": "778ef68b5270657f75185b27dc8219b35847afa1",
"index": 5829,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef demo(myAPI):\n myAPI.setAttr()\n video_capture = cv2.VideoCapture(0)\n print('Press q to quit: ')\n while True:\n ret, frame = video_capture.read()\n fra... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def f1(phi, phi_o, d):
"""sinusoidally growing function between (phi_o-d) to phi_o"""
return 1 - sigmoid_decay(phi, phi_o, d)
def f2(phi, sigma):
"""normal distribution"""
return math.exp(-phi ** 2 / sigma ** 2)
<|reserved_special_token_0|>
<|reserved_special_token_1... | flexible | {
"blob_id": "19bb3cd0c7862f39a78479d9a9703ebef198fc73",
"index": 3677,
"step-1": "<mask token>\n\n\ndef f1(phi, phi_o, d):\n \"\"\"sinusoidally growing function between (phi_o-d) to phi_o\"\"\"\n return 1 - sigmoid_decay(phi, phi_o, d)\n\n\ndef f2(phi, sigma):\n \"\"\"normal distribution\"\"\"\n retu... | [
2,
3,
4,
5,
6
] |
from django.contrib import admin
from django.urls import path
from . import views
urlpatterns = [
path('', views.artifact, name="artifacts"),
path('<int:artifact_id>', views.detail, name="detail"),
path('register/', views.register, name="register")
] | normal | {
"blob_id": "9b73037e8af7d4f91261cebf895b68650182fcd5",
"index": 2780,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', views.artifact, name='artifacts'), path(\n '<int:artifact_id>', views.detail, name='detail'), path('register/',\n views.register, name='register')]\n",
"st... | [
0,
1,
2,
3
] |
from collections import defaultdict, namedtuple
from color import RGB, clamp
import math
import controls_model as controls
from eyes import Eye, MutableEye
from geom import ALL
#from icicles.ice_geom import ALL
def load_geometry(mapfile):
"""
Load sheep neighbor geometry
Returns a map { panel: [(edge-ne... | normal | {
"blob_id": "fe01b78d29dc456f7a537dd5639bc658fc184e36",
"index": 5035,
"step-1": "from collections import defaultdict, namedtuple\nfrom color import RGB, clamp\n\nimport math\n\nimport controls_model as controls\nfrom eyes import Eye, MutableEye\n\nfrom geom import ALL\n#from icicles.ice_geom import ALL\n\ndef l... | [
0
] |
import os
from subprocess import Popen, PIPE
from Bio import SeqIO
from Bio.Align.Applications import ClustalOmegaCommandline
from Bio import Phylo
from io import StringIO
# from ete3 import Tree, TreeStyle
import pylab
class TreeDrawer:
def __init__(self, sequences=None):
self.sequences = sequences
... | normal | {
"blob_id": "5adb16c654a4e747f803590c42328fa6ba642e95",
"index": 7599,
"step-1": "<mask token>\n\n\nclass TreeDrawer:\n <mask token>\n <mask token>\n\n def draw_tree(self, filename):\n tree_file = open('dnaml.tree')\n x = tree_file.read()\n tree = Phylo.read(StringIO(x[:-2]), 'newic... | [
2,
3,
4,
5,
6
] |
# MINISTを読み込んでレイヤーAPIでCNNを構築するファイル
import tensorflow as tf
import numpy as np
import os
import tensorflow as tf
import glob
import numpy as np
import config as cf
from data_loader import DataLoader
from PIL import Image
from matplotlib import pylab as plt
dl = DataLoader(phase='Train', shuffle=True)
X... | normal | {
"blob_id": "a5559ff22776dee133f5398bae573f515efb8484",
"index": 3820,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndl = DataLoader(phase='Train', shuffle=True)\nX_data, y_data = dl.shuffle_and_get()\nX_data = np.reshape(X_data, [-1, cf.Height, cf.Width])\nconfig = tf.ConfigProto()\nconfig.gpu_options.... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def plot_depth_slice(x, depth, fld, stretch_depth=-500, plot_type=
'pcolormesh', cmap='YlOrRd', title=None, cmin=None, cmax=None, dpi=100,
show_colorbar=True):
"""2D plot of depth vs some other variable, stretching first 500m of depth.
Parameters
----------
depth ... | flexible | {
"blob_id": "b039ed74e62f3a74e8506d4e14a3422499046c06",
"index": 860,
"step-1": "<mask token>\n\n\ndef plot_depth_slice(x, depth, fld, stretch_depth=-500, plot_type=\n 'pcolormesh', cmap='YlOrRd', title=None, cmin=None, cmax=None, dpi=100,\n show_colorbar=True):\n \"\"\"2D plot of depth vs some other va... | [
1,
2,
3,
4,
5
] |
import sys
if sys.version_info.major == 2:
from itertools import izip
else:
izip = zip
| normal | {
"blob_id": "88445d8466d7acbf29d2525c7e322611d66494cd",
"index": 8315,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif sys.version_info.major == 2:\n from itertools import izip\nelse:\n izip = zip\n",
"step-3": "import sys\nif sys.version_info.major == 2:\n from itertools import izip\nelse:\... | [
0,
1,
2
] |
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
from PyQt5.QtSql import *
from DatabaseHandler import send_answer
class PW(QWidget):
def __init__(self, index, question, pid):
super().__init__()
self.question = question
self.pid = pid
self.maxim = len(self.questi... | normal | {
"blob_id": "f35569e2d8d26f43d4b2395b5088902c6cd3b826",
"index": 2232,
"step-1": "<mask token>\n\n\nclass PW(QWidget):\n <mask token>\n <mask token>\n\n def ButtonNoAction(self):\n table = 'patient_' + str(self.pid)\n send_answer(self.question[self.index]['qid'], 'Нет', table)\n if ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
driver.maximize_window()
driver.get('http://www.toolsqa.com/iframe-practice-page/')
driver.switch_to.default_content()
driver.find_element_by_xpath("//span[text()='VIDEOS']").click()
<|reserved_special_token_1|>
<|reserved_spec... | flexible | {
"blob_id": "53eb1dcd54ce43d9844c48eb1d79f122a87dca39",
"index": 3831,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndriver.maximize_window()\ndriver.get('http://www.toolsqa.com/iframe-practice-page/')\ndriver.switch_to.default_content()\ndriver.find_element_by_xpath(\"//span[text()='VIDEOS']\").click()... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
send(ip / ack_packet)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
ip = IP(src=sys.argv[1], dst=sys.argv[2])
syn_packet = TCP(sport=52255, dport=1237, flags='S', seq=100, options=[(
'MSS', 689), ('WScale', 1)])... | flexible | {
"blob_id": "acd6197e60cf59ffcaa33bb50a60a03592bb3559",
"index": 7169,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsend(ip / ack_packet)\n",
"step-3": "<mask token>\nip = IP(src=sys.argv[1], dst=sys.argv[2])\nsyn_packet = TCP(sport=52255, dport=1237, flags='S', seq=100, options=[(\n 'MSS', 689), ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
KEYS = ['CM', 'GM']
NOTES_FOR_KEY = {'CM': [21, 23, 24, 26, 28, 29, 31, 33, 35, 36, 38, 40, 41,
43, 45, 47, 48, 50, 52, 53, 55, 57, 59, 60, 62, 64, 65, 67, 69, 71, 72,
74, 76, 77, 79, 81, 83, 84, 86, 88, 89, 91, 93, 95, 96... | flexible | {
"blob_id": "dd7ade05ef912f7c094883507768cc21f95f31f6",
"index": 533,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nKEYS = ['CM', 'GM']\nNOTES_FOR_KEY = {'CM': [21, 23, 24, 26, 28, 29, 31, 33, 35, 36, 38, 40, 41,\n 43, 45, 47, 48, 50, 52, 53, 55, 57, 59, 60, 62, 64, 65, 67, 69, 71, 72,\n 74, 76, 7... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for line in file1:
x_list.append(float(line))
<|reserved_special_token_0|>
for line in file2:
y_list.append(float(line))
file2.close
file1.close
<|reserved_special_token_0|>
plt.plot(x_list, y_list, label='robot trajectory... | flexible | {
"blob_id": "d869aa32cb9793ce11a5b6a782cc66c2dd0be309",
"index": 6176,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in file1:\n x_list.append(float(line))\n<mask token>\nfor line in file2:\n y_list.append(float(line))\nfile2.close\nfile1.close\n<mask token>\nplt.plot(x_list, y_list, labe... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def add_owner_mce(m) ->MetadataChangeEventClass:
entity = m['Table']
schema = m['Schema']
dataset_name = f'{schema}.{entity}'
owners = [OwnerClass(owner=owner, type=OwnershipTypeClass.DATAOWNER) for
owner in m['Owner']]
changed_snapshot = DatasetSnapshotClass(u... | flexible | {
"blob_id": "7ad5e803afa42790e878bfb923eddcfde2d21928",
"index": 1501,
"step-1": "<mask token>\n\n\ndef add_owner_mce(m) ->MetadataChangeEventClass:\n entity = m['Table']\n schema = m['Schema']\n dataset_name = f'{schema}.{entity}'\n owners = [OwnerClass(owner=owner, type=OwnershipTypeClass.DATAOWNER... | [
2,
3,
4,
5,
6
] |
# -*- coding:utf-8 -*-
#实现同义词词林的规格化
with open('C:\\Users\\lenovo\\Desktop\\哈工大社会计算与信息检索研究中心同义词词林扩展版.txt') as f:
with open('convert.txt','a') as w:
for line in f:
data = line[8:-1].split()
for item in data:
tmp = data.copy()
... | normal | {
"blob_id": "9109e649a90730df022df898a7760140275ad724",
"index": 4854,
"step-1": "<mask token>\n",
"step-2": "with open('C:\\\\Users\\\\lenovo\\\\Desktop\\\\哈工大社会计算与信息检索研究中心同义词词林扩展版.txt') as f:\n with open('convert.txt', 'a') as w:\n for line in f:\n data = line[8:-1].split()\n ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
res.read_rcf()
res.read_his()
<|reserved_special_token_0|>
for kt, step in enumerate(res.steps):
if step.conv_status in [-1]:
if step.time in tx:
tsteps.append(kt)
<|reserved_special_token_0|>
res.read_dat(... | flexible | {
"blob_id": "fb6dd9ec7d8dc80eace90dadc2112c7c27125efd",
"index": 2055,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nres.read_rcf()\nres.read_his()\n<mask token>\nfor kt, step in enumerate(res.steps):\n if step.conv_status in [-1]:\n if step.time in tx:\n tsteps.append(kt)\n<mask to... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def upload_to_s3(file_name, node_number):
try:
key_info_json = open('awsinfo.json').read()
except FileNotFoundError:
print('awsinfo.json is not exist in dir.')
exit(-1)
data = json.loads(key_i... | flexible | {
"blob_id": "2f0d611fecdb5717029938d2ec2cd2db345b8f3a",
"index": 8176,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef upload_to_s3(file_name, node_number):\n try:\n key_info_json = open('awsinfo.json').read()\n except FileNotFoundError:\n print('awsinfo.json is not exist in di... | [
0,
1,
2,
3
] |
from typing import Sequence
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
def plot3D(X, Y, Z, proporcao=1, espelharZ = False):
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_xlabel('X ')
ax.set_ylabel('Y ')
ax.set_zlabel('Z ')
np.floor
col... | normal | {
"blob_id": "ff20b65f35614415ad786602c0fc2cabd08124fb",
"index": 4065,
"step-1": "<mask token>\n\n\ndef limitZ(Z, limit=10):\n for i in range(len(Z)):\n for j in range(len(Z[i])):\n if Z[i][j] > limit:\n Z[i][j] = np.inf\n if Z[i][j] < -limit:\n Z[i][... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def save_df_surnames_as_pickle():
df_surnames, df_categories = load_surnames()
df = shuffle(df_surnames, random_state=sc.RANDOM_STATE)
train_cnt = int(df['surname'].count() * sc.TRAIN_TEST_RATIO)
train = df[0:train_cnt]
test = df[train_cnt + 1:]
df_surnames.to_pick... | flexible | {
"blob_id": "db46fbfb1acd855eebb5c9f557d70038b84e812d",
"index": 8573,
"step-1": "<mask token>\n\n\ndef save_df_surnames_as_pickle():\n df_surnames, df_categories = load_surnames()\n df = shuffle(df_surnames, random_state=sc.RANDOM_STATE)\n train_cnt = int(df['surname'].count() * sc.TRAIN_TEST_RATIO)\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open('txt.txt', 'r') as f:
data = f.readlines()
line = 0
for i in range(10, 110, 10):
agg = 0
for j in range(num_tests):
agg += int(data[line])
line += 1
res.append(... | flexible | {
"blob_id": "176ffac7ad47f5c43a24acc664631f8353ec5100",
"index": 967,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('txt.txt', 'r') as f:\n data = f.readlines()\n line = 0\n for i in range(10, 110, 10):\n agg = 0\n for j in range(num_tests):\n agg += int(data[... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
marks = {'S': 'subject', 'O': 'object', 'A': 'attribute', 'C': 'clause'}
marks_reverse = {'subject': 'S', 'object': 'O', 'attribute': 'A', 'clause': 'C'
}
<|reserved_special_token_1|>
marks = {
"S":"subject",
"O":"object",
"A":"attribute",
... | flexible | {
"blob_id": "c66b07c45f4a675a6c7fcec82048a3197910d0d8",
"index": 3435,
"step-1": "<mask token>\n",
"step-2": "marks = {'S': 'subject', 'O': 'object', 'A': 'attribute', 'C': 'clause'}\nmarks_reverse = {'subject': 'S', 'object': 'O', 'attribute': 'A', 'clause': 'C'\n }\n",
"step-3": "marks = {\n \"S\":\"... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class Profile(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Profile(models.Model):
<|reserved_special_token_0|>
def __str__(self):
return f'{self.user.username} Profile... | flexible | {
"blob_id": "51ff1181f0ddac3a8f7cbd9f9d2eedae29a6c559",
"index": 6654,
"step-1": "<mask token>\n\n\nclass Profile(models.Model):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Profile(models.Model):\n <mask token>\n\n def __str__(self):\n return f'{self.user.username} P... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
##############################################################################
#
# Copyright (C) 2011 Eficent (<http://www.eficent.com/>)
# Jordi Ballester Alomar <jordi.ballester@eficent.com>
#
# This program is free software: you can redistribute it and/or modify
# it und... | normal | {
"blob_id": "1ddec426e4ad50f1d0e8a57ed841fbdf8c51b00f",
"index": 9871,
"step-1": "<mask token>\n\n\nclass tax(osv.Model):\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass tax(osv.Model):\n _inherit = 'sgr.tax'\n\n def send_alerts(self, cr, uid... | [
1,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
def read_dataset(mode, args):
def decode_example(protos, vocab_size):
features = {'key': tf.FixedLenFeature(shape=[1], dtype=tf.int64),
'indices': tf.VarLenFeature(dtype=tf.int64), 'values': tf.
VarLenFeature(dtype=tf.float32)}
parsed_features ... | flexible | {
"blob_id": "fb9ae5b3cdeac0c254669e214779ad43a02bff6d",
"index": 4596,
"step-1": "<mask token>\n\n\ndef read_dataset(mode, args):\n\n def decode_example(protos, vocab_size):\n features = {'key': tf.FixedLenFeature(shape=[1], dtype=tf.int64),\n 'indices': tf.VarLenFeature(dtype=tf.int64), 'va... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
execute('scrapy crawl laptop'.split())
<|reserved_special_token_1|>
import os, sys
from scrapy.cmdline import execute
sys.path.append(os.path.dirname(os.path.abspath(_... | flexible | {
"blob_id": "71ff8e8a62a3b2731071ed7a039b51c150ebaca4",
"index": 3671,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nexecute('scrapy crawl laptop'.split())\n",
"step-3": "import os, sys\nfrom scrapy.cmdline import execute\nsys.path.append(os... | [
0,
1,
2
] |
from django.contrib import admin
from .models import User
# Register your models here.
@admin.register(User)
class AuthorizationUserAdmin(admin.ModelAdmin):
exclude = ['open_id']
pass
| normal | {
"blob_id": "d3585e7b761fa7b2eeaacf09f84bb6a4abc1cf02",
"index": 6806,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@admin.register(User)\nclass AuthorizationUserAdmin(admin.ModelAdmin):\n <mask token>\n pass\n",
"step-3": "<mask token>\n\n\n@admin.register(User)\nclass AuthorizationUserAdm... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Imprimidor(Thread):
def __init__(self, nombre, berlin, bolsa_dinero):
super().__init__()
pass
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ... | flexible | {
"blob_id": "ab79e2f9584dbbb526c62bde882a1bc9874b56f9",
"index": 7903,
"step-1": "<mask token>\n\n\nclass Imprimidor(Thread):\n\n def __init__(self, nombre, berlin, bolsa_dinero):\n super().__init__()\n pass\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\... | [
2,
4,
5,
6,
7
] |
import cv2
import imutils
import detect
def detectByPathVideo(path, writer):
video = cv2.VideoCapture(path)
check, frame = video.read()
if check == False:
print('Video Not Found. Please Enter a Valid Path (Full path of Video Should be Provided).')
return
print('Detecting p... | normal | {
"blob_id": "5044b8bc8cabd7762df6a0327828df4546ab8d96",
"index": 9000,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef detectByPathVideo(path, writer):\n video = cv2.VideoCapture(path)\n check, frame = video.read()\n if check == False:\n print(\n 'Video Not Found. Please... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def intersection(list1, list2):
return set(list1).intersection(list2)
def computeSteps(x, y, step, steps):
curr = 0
if (x, y) in steps:
curr = steps.get((x, y))
steps[x, y] = step + curr
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_sp... | flexible | {
"blob_id": "e9e119dd69f9416e007e748d7f494741140efc8e",
"index": 8182,
"step-1": "<mask token>\n\n\ndef intersection(list1, list2):\n return set(list1).intersection(list2)\n\n\ndef computeSteps(x, y, step, steps):\n curr = 0\n if (x, y) in steps:\n curr = steps.get((x, y))\n steps[x, y] = step... | [
2,
4,
5,
6,
7
] |
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.feature_selection import SelectKBest, chi2
from sklearn import metrics, ensemble, linear_model, svm
from numpy import log, ones, array, zeros, mean, std, repeat
import numpy as np
import scipy.sparse as sp
import re
import csv
fro... | normal | {
"blob_id": "91eb0ae8e59f24aeefdabd46546bc8fb7a0b6f6c",
"index": 3833,
"step-1": "<mask token>\n\n\ndef normalize(f, lammatize=False):\n f = [x.lower() for x in f]\n f = [x.replace('\\\\n', ' ') for x in f]\n f = [x.replace('\\\\t', ' ') for x in f]\n f = [x.replace('\\\\xa0', ' ') for x in f]\n f... | [
7,
8,
9,
10,
12
] |
<|reserved_special_token_0|>
class ListVolumeType(command.Lister):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class ShowVolumeType(command.ShowOne):
def get_parser(self, prog_name):
parser = super(ShowVolumeType, self).get_parser(prog_name)
parser.add_argument('volume_typ... | flexible | {
"blob_id": "c73bea686786a30f298500968cfd01e2d5125d75",
"index": 4013,
"step-1": "<mask token>\n\n\nclass ListVolumeType(command.Lister):\n <mask token>\n <mask token>\n\n\nclass ShowVolumeType(command.ShowOne):\n\n def get_parser(self, prog_name):\n parser = super(ShowVolumeType, self).get_parse... | [
4,
5,
6,
7,
8
] |
import os
import shutil
import json
from django.shortcuts import render, HttpResponse
from django.utils.encoding import escape_uri_path
from django.db import transaction
from web_pan.settings import files_folder
from disk import models
# Create your views here.
def logined(func):
def wrapper(request, *args, **k... | normal | {
"blob_id": "eeb87891d1a02484a61537745ec6f13387017929",
"index": 705,
"step-1": "<mask token>\n\n\ndef logined(func):\n\n def wrapper(request, *args, **kwargs):\n session = request.session.get('user')\n if not session:\n return render(request, 'login.html')\n else:\n ... | [
9,
10,
12,
13,
14
] |
from functions.service_funcs.get_data import get_data_character
def clean_room(update):
char, db_sess = get_data_character(update, return_sess=True)
# удаляем старую комнату и всю инфу о ней
if char and char.room:
if char.room.mobs:
for mob in char.room.mobs:
db_sess.de... | normal | {
"blob_id": "4d57fa22282d7b3f8adabedd7a04e32767181890",
"index": 5693,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef clean_room(update):\n char, db_sess = get_data_character(update, return_sess=True)\n if char and char.room:\n if char.room.mobs:\n for mob in char.room.mob... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Comment(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __str__(self):
return self.text
class Rating(models.Model):
rating = models.PositiveIntegerField()
pr... | flexible | {
"blob_id": "052574be3f4a46bceefc0a54b1fe268a7cef18a9",
"index": 3061,
"step-1": "<mask token>\n\n\nclass Comment(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __str__(self):\n return self.text\n\n\nclass Rating(models.Model):\n rating = models.Positi... | [
8,
9,
10,
13,
14
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.1 on 2016-10-28 17:50
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations... | normal | {
"blob_id": "b0064a5cd494d5ad232f27c63a4df2c56a4c6a66",
"index": 5241,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Favorits(QDialog, Ui_DialogFavorit):
<|reserved_special_token_0|>
def __init__(self):
super(Favorits, self).__init__()
self.setupUi(self)
self.buttonBox.button(QDialogButtonBox.Save).setText('Сохранить')
self.buttonBox.button(QDialogButtonBox... | flexible | {
"blob_id": "14023785983f493af57189b3d96254efef2e33ae",
"index": 8180,
"step-1": "<mask token>\n\n\nclass Favorits(QDialog, Ui_DialogFavorit):\n <mask token>\n\n def __init__(self):\n super(Favorits, self).__init__()\n self.setupUi(self)\n self.buttonBox.button(QDialogButtonBox.Save).s... | [
4,
5,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
received_event = Event()
leave_rooms_event = Event()
exit_event = Event()
output_message_queue = AGQueue()
input_message_queue = AGQueue()
matrix_to_aio_queue = AGQueue()
aio_to_matrix_queue = AGQueue()
sync_to_matrix_queue = Queu... | flexible | {
"blob_id": "af1a6c6009b21962228fbe737f27c22bf9460762",
"index": 729,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nreceived_event = Event()\nleave_rooms_event = Event()\nexit_event = Event()\noutput_message_queue = AGQueue()\ninput_message_queue = AGQueue()\nmatrix_to_aio_queue = AGQueue()\naio_to_matr... | [
0,
1,
2,
3
] |
#header
import matplotlib.pyplot as pmf
import random
p = 0.5 # Probablility of success for original system
n = 18 # Number of trials
Y = [] # Contains binomial RVs
b = [0] * (n+1) # List of n + 1 zeroes
N = 100 # Number of experiments performed
for j in range(N):
# Bernoulli random variable
for i in ra... | normal | {
"blob_id": "9a1b268386b4652bf50af0365892ef7338329727",
"index": 9631,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor j in range(N):\n for i in range(n):\n r = random.uniform(0, 1)\n if r < p:\n x = 1\n else:\n x = 0\n Y.append(x)\n outcome = su... | [
0,
1,
2,
3,
4
] |
##
## Originally created by https://www.reddit.com/user/AlekseyP
## Seen at: https://www.reddit.com/r/technology/comments/43fi39/i_set_up_my_raspberry_pi_to_automatically_tweet
##
#!/usr/bin/python
import os
import sys
import csv
import datetime
import time
import twitter
#Configuration
# Twitter
ACCESS_TOKEN=""
ACCE... | normal | {
"blob_id": "6492f1eda79fd3116058f29647dc5f09e903f637",
"index": 7274,
"step-1": "##\n## Originally created by https://www.reddit.com/user/AlekseyP\n## Seen at: https://www.reddit.com/r/technology/comments/43fi39/i_set_up_my_raspberry_pi_to_automatically_tweet\n##\n\n#!/usr/bin/python\nimport os\nimport sys\nimp... | [
0
] |
<|reserved_special_token_0|>
def get_text_from_image(imageName):
img = preprocess(imageName)
result = tes.image_to_string(img)
return result
<|reserved_special_token_0|>
def find_receipt_box(image):
"""
Finds a contour around the receipt in the given image.
Returns the bounding box and the... | flexible | {
"blob_id": "e480136aca96e45cc8a7ca34c1a9d09b96a5a4da",
"index": 4152,
"step-1": "<mask token>\n\n\ndef get_text_from_image(imageName):\n img = preprocess(imageName)\n result = tes.image_to_string(img)\n return result\n\n\n<mask token>\n\n\ndef find_receipt_box(image):\n \"\"\"\n Finds a contour a... | [
5,
7,
8,
9,
10
] |
from packages import data as DATA
from packages import plot as PLOT
from packages import universal as UNIVERSAL
from packages import currency_pair as CP
import matplotlib.pyplot as plt
import mpl_finance as mpf
from packages import db as DB
import CONSTANTS
import datetime
from matplotlib.pylab import date2num
from mat... | normal | {
"blob_id": "9aaaa744780dbd32b14e09a34976a2a0a3ce34f7",
"index": 7864,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor cnt in range(20, len(rows)):\n row_previous2 = rows[cnt - 2]\n row_previous1 = rows[cnt - 1]\n row = rows[cnt]\n open = row[2]\n high = row[3]\n low = row[4]\n cl... | [
0,
1,
2,
3,
4
] |
import os
import sqlite3
import datetime
directory = 'C:\PyHelp'
if not os.path.exists(directory):
os.makedirs(directory)
rand_facts = '''• Exception is used as a base class for all exceptions. It's strongly recommended (but not yet required) that user exceptions are derived from this class too.
• System... | normal | {
"blob_id": "a2c93fd632a637d47f05e0a4fda851b465d03a31",
"index": 4674,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif not os.path.exists(directory):\n os.makedirs(directory)\n<mask token>\nif not file_exists:\n x = open(op, 'w')\n x.write(rand_facts)\n",
"step-3": "<mask token>\ndirectory =... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
year = datetime.datetime.now().year
project = 'python201'
copyright = f'2019-{year} Geoffrey Lentner, 2018 Ashwin Srinath'
author = 'Geoffrey Lentner, Ashwin Srinath'
version = '0.0.1'
release = '0.0.1'
extensions = ['sphinx.ext.i... | flexible | {
"blob_id": "1ead23c6ea4e66b24e60598ae20606e24fa41482",
"index": 1024,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nyear = datetime.datetime.now().year\nproject = 'python201'\ncopyright = f'2019-{year} Geoffrey Lentner, 2018 Ashwin Srinath'\nauthor = 'Geoffrey Lentner, Ashwin Srinath'\nversion = '0.0.1... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class ColorPoint(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __getitem__(self, item) ->float:
"""
>>> cp = ColorPoint(Color('#880073'), Color('white'), '')
>>> cp[0] # hue
0.8590686274509803
>>> cp[1] # satu... | flexible | {
"blob_id": "e239c2089fc6d4ab646c490b6e3de8953cec5634",
"index": 8093,
"step-1": "<mask token>\n\n\nclass ColorPoint(object):\n <mask token>\n <mask token>\n\n def __getitem__(self, item) ->float:\n \"\"\"\n >>> cp = ColorPoint(Color('#880073'), Color('white'), '')\n >>> cp[0] # hu... | [
7,
8,
9,
10,
12
] |
<|reserved_special_token_0|>
@ddt.ddt
class TestAddress(unittest.TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def test_02_check_address(self):
url = 'http://ecshop.itsoso.cn/ECMobile/?url=/address/list'
... | flexible | {
"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|>
def write_csv(filename, data_list):
"""
将python对象 [{}, {}. {}, {} ...] 写入到csv文件中
:param filename: 生成的csv文件名
:param data_list: [{}, {}. {}, {} ...]
:return: None
"""
with open(filename, 'w') as f:
dict_writer = csv.DictWriter(f, data_list[0].keys())
... | flexible | {
"blob_id": "7531480f629c1b3d28210afac4ef84b06edcd420",
"index": 3825,
"step-1": "<mask token>\n\n\ndef write_csv(filename, data_list):\n \"\"\"\n 将python对象 [{}, {}. {}, {} ...] 写入到csv文件中\n :param filename: 生成的csv文件名\n :param data_list: [{}, {}. {}, {} ...]\n :return: None\n \"\"\"\n with ... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def sentiment(text):
global url
global headers
body = {'text': text}
try:
r = requests.post(url, headers=headers, data=json.dumps(body))
dic = r.json()
except Exception as e:
print('分析失败')
pass
time.sleep(0.3)
return dic['items']... | flexible | {
"blob_id": "a95e64877a1fc9f8109f1293b4ae9176f4f64647",
"index": 3090,
"step-1": "<mask token>\n\n\ndef sentiment(text):\n global url\n global headers\n body = {'text': text}\n try:\n r = requests.post(url, headers=headers, data=json.dumps(body))\n dic = r.json()\n except Exception a... | [
1,
2,
3,
4,
5
] |
n, k = map(int, input().split())
k_list = []
for i in range(k):
l, r = map(int, input().split())
k_list.append([l, r])
dp = [0] * (n + 1)
dp[1] = 1
dpsum = [0] * (n + 1)
dpsum[1] = 1
for i in range(1, n):
dpsum[i] = dp[i] + dpsum[i - 1]
for j in range(k):
l, r = k_list[j]
li = i + l
... | normal | {
"blob_id": "97720baab961d50ceae832d52350b9871c552c84",
"index": 9071,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(k):\n l, r = map(int, input().split())\n k_list.append([l, r])\n<mask token>\nfor i in range(1, n):\n dpsum[i] = dp[i] + dpsum[i - 1]\n for j in range(k):\n ... | [
0,
1,
2
] |
import pymysql
db = pymysql.connect( "localhost", "root", "", "order_db",
use_unicode=True, charset="utf8")
cursor = db.cursor()
sql = "DROP TABLE custdetail"
cursor.execute(sql)
db.close()
| normal | {
"blob_id": "1aa2bff245322a34438cc836e23f430926dfac6c",
"index": 3414,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncursor.execute(sql)\ndb.close()\n",
"step-3": "<mask token>\ndb = pymysql.connect('localhost', 'root', '', 'order_db', use_unicode=True,\n charset='utf8')\ncursor = db.cursor()\nsql ... | [
0,
1,
2,
3,
4
] |
def mysum(*c):
print(sum([x for x in c]))
mysum(1,2,3,4,0xB) | normal | {
"blob_id": "2c4fa92b28fa46a26f21ada8826474baac204e00",
"index": 1234,
"step-1": "<mask token>\n",
"step-2": "def mysum(*c):\n print(sum([x for x in c]))\n\n\n<mask token>\n",
"step-3": "def mysum(*c):\n print(sum([x for x in c]))\n\n\nmysum(1, 2, 3, 4, 11)\n",
"step-4": "def mysum(*c):\n print(su... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
parser.add_argument('nex', help='path of the .nex file to be launched')
parser.add_argument('file', help='autoexec.bas file to be generated')
<|reserved_special_token_0|>
contents += bytearray((0, 10))
contents += struct.pack('<H'... | flexible | {
"blob_id": "0744ec646e7b9303c67c25dff2997568c6171b91",
"index": 108,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('nex', help='path of the .nex file to be launched')\nparser.add_argument('file', help='autoexec.bas file to be generated')\n<mask token>\ncontents += bytearray((0, 10))... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def factor(n):
result = []
d = 2
while d * d <= n:
if n % d == 0:
result.append(d)
n //= d
else:
d += 1
if n > 1:
result.append(n)
return result
def get_coeff(period):
c = randint(0, period)
while gc... | flexible | {
"blob_id": "11e9d25c30c8c9945cfa3c234ffa1aab98d1869e",
"index": 8023,
"step-1": "<mask token>\n\n\ndef factor(n):\n result = []\n d = 2\n while d * d <= n:\n if n % d == 0:\n result.append(d)\n n //= d\n else:\n d += 1\n if n > 1:\n result.append... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def GenTests(api):
yield api.test('basic')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def RunSteps(api):
try:
api.step('test step', [{}])
except AssertionError as e:
assert str(e) ... | flexible | {
"blob_id": "25d210144ef209fd5e4ff7e4e4c2e77fd7eb79ac",
"index": 3480,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef GenTests(api):\n yield api.test('basic')\n",
"step-3": "<mask token>\n\n\ndef RunSteps(api):\n try:\n api.step('test step', [{}])\n except AssertionError as e:\n... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
pygame.init()
<|reserved_special_token_0|>
pygame.display.set_caption('Social Force Model - Crosswalk')
<|reserved_special_token_0|>
for line in open(WALLSFILE, newline='', encoding='utf-8-sig'):
coords = line.split(',')
w... | flexible | {
"blob_id": "00051a4087bfcf2e6826e9afa898830dc59aa5ab",
"index": 5451,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npygame.init()\n<mask token>\npygame.display.set_caption('Social Force Model - Crosswalk')\n<mask token>\nfor line in open(WALLSFILE, newline='', encoding='utf-8-sig'):\n coords = line.... | [
0,
1,
2,
3,
4
] |
def word_count(s):
# Your code here
cache = {}
ignore = '":;,.-+=/\\|[]{}()*^&'
lower = s.lower()
for i in lower:
if i in ignore:
lower = lower.replace(i, '')
words = lower.split()
for j in words:
if j not in cache:
cache[j] = 1
else:
... | normal | {
"blob_id": "97d84f99264afa5e7df4b5d22cf4c49b2d14ff7a",
"index": 8291,
"step-1": "<mask token>\n",
"step-2": "def word_count(s):\n cache = {}\n ignore = '\":;,.-+=/\\\\|[]{}()*^&'\n lower = s.lower()\n for i in lower:\n if i in ignore:\n lower = lower.replace(i, '')\n words = l... | [
0,
1,
2,
3
] |
from django import forms
from .models import File, Sample, Plate, Well, Machine, Project
class MachineForm(forms.ModelForm):
class Meta:
model = Machine
fields = ['name', 'author', 'status', 'comments']
class ProjectForm(forms.ModelForm):
class Meta:
model = Project
field... | normal | {
"blob_id": "5bb894feaf9293bf70b3f831e33be555f74efde8",
"index": 6901,
"step-1": "<mask token>\n\n\nclass SampleForm(forms.ModelForm):\n\n\n class Meta:\n model = Sample\n fields = ['name', 'alias', 'sample_type', 'description', 'project',\n 'author', 'sequence', 'length', 'genbank', ... | [
3,
5,
6,
7
] |
import math
from chainer import cuda
from chainer import function
from chainer.functions import Sigmoid
from chainer.utils import type_check
import numpy
def _as_mat(x):
if x.ndim == 2:
return x
return x.reshape(len(x), -1)
class Autoencoder(function.Function):
def __init__(self, in_size, hidde... | normal | {
"blob_id": "97eb599ae8bf726d827d6f8313b7cf2838f9c125",
"index": 4098,
"step-1": "<mask token>\n\n\nclass Autoencoder(function.Function):\n <mask token>\n\n def hidden(self, x):\n h = _Encoder(self.W, self.b1)(x)\n if self.activation is not None:\n h = self.activation(h)\n h... | [
11,
13,
14,
15,
17
] |
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