standbyme

import os
for i in range(1,10):
    dir_name = str(i)
    os.mkdir(dir_name)
    os.chdir(dir_name)
    open("question", 'a').close()
    open("input", 'a').close()
    open("output", 'a').close()
    open("whatsup", 'a').close()
    os.mkdir("code")

standbyme

from string import Template

with open("template.md", 'r') as file:
    templateFile = file.read()
with open("main.c", 'r') as file:
    code = file.read()

template = Template(templateFile)
print(template.safe_substitute(code=code))

standbyme

import sys
from PyPDF2 import PdfFileMerger

PDFMerger = PdfFileMerger()
filePathList = sys.argv[1:]

for filePath in filePathList:
    PDFMerger.append(filePath)
with open("Report.pdf", "wb") as output:
    PDFMerger.write(output)

standbyme

import http.client
import urllib.request
import urllib.parse
import urllib.error
import base64
import json

sub_key = "ba0747d02f2a42ffbc0f0dd2cb1fba06"
KBID = "b49a4b5a-8a4e-4feb-9caa-604ea9a6ba82"

standbyme

hbase(main):003:0> put 'alarm_mmdd','imeiTSE','r:stat','stat'
0 row(s) in 0.1400 seconds

hbase(main):004:0> put 'alarm_mmdd','imeiTSE','r:type','type'
0 row(s) in 0.0060 seconds

hbase(main):005:0> put 'alarm_mmdd','imeiTSE','r:viewed','viewed'
0 row(s) in 0.0020 seconds

hbase(main):006:0> put 'alarm_mmdd','imeiTSE','r:record','record'

standbyme

import re


def solve_specific(line: str):
    matched = re.match(r'\[(.*)\]\[(.*?)\]', line)
    name = matched.group(1)
    label = matched.group(2)

    return '\n'.join(list(map(lambda x: '{} B-{}'.format(x[1], label) if x[0] == 0 else '{} I-{}'.format(x[1], label), enumerate(name))))

standbyme

from torch.utils.data import Dataset, DataLoader

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class CSVDataset(Dataset):
    def __init__(self, x, y):
        self.data = torch.tensor(x, dtype=torch.float, device=device)
        self.target = torch.tensor(y, dtype=torch.long, device=device)

standbyme

def tf__helper(self, inputs):
  do_return = False
  retval_ = ag__.UndefinedReturnValue()
  inputs = ag__.converted_call('transpose', tf, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), (inputs, [1, 0, 2]), None)
  batch_size = ag__.converted_call('shape', tf, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), (inputs,), None)[1]
  hidden = ag__.converted_call('zeros', tf, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), ([batch_size, hidden_size],), {'name': 'hidden'})
  output = ag__.converted_call('zeros', tf, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), ([batch_size, output_size],), {'name': 'output'})

  def loop_body(loop_vars, hidden_1, output_1):
    x_t = loop_vars

standbyme

class MyModel(tf.keras.Model):
  def __init__(self, keep_probability=0.2):
    super(MyModel, self).__init__()
    self.dense1 = tf.keras.layers.Dense(4)
    self.dense2 = tf.keras.layers.Dense(5)
    self.keep_probability = keep_probability

  @tf.function
  def call(self, inputs, training=True):
    y = self.dense2(self.dense1(inputs))