leimao

from __future__ import annotations
from typing import IO
import asyncio
import sys
import contextlib
import aiofiles.threadpool

from chat_streams import split_lines, write, handle_writes

async def handle_reads(reader: asyncio.StreamReader) -> None:

leimao

#include "class.h"

Test::Test(int a, int b): mA{a}, mB{b}
{
}

int Test::getA()
{
    return this->mA;
}

leimao

#include <iostream>
#include <limits>
using namespace std;

int main(){
	char var[10];
	bool valid = false;

	while(!valid){
		cout << "Enter a string 9 characters long: ";

leimao

import onnxruntime
import numpy as np
import time
from onnxruntime.datasets import get_example

# ONNXRuntime API Documentation
# https://microsoft.github.io/onnxruntime/python/api_summary.html

log_severity_level = 0
model_name = "logreg_iris.onnx"

leimao

import threading 
import multiprocessing
import time
import random


class ParallelCounter(object):
    """
    ParallelCounter instance would have num_counter counters, and num_worker workers. 
    Each worker would contribute to all counters. 

leimao

import module

class Chinese(object):
    
    # Class attributes
    nationality = "China"

    # Instance attributes
    def __init__(self, name, birth_year):

leimao

# The object after * must be a iterator
# * is used to unpack a iterator 
list_1 = [0,1,2]
print(list_1)
# Iterate  list
print(*list_1)

print("*"*50)

leimao

Git/Github step-by-step Workflow

Step-by-step guide for creating a feature or bugfix branch, submit it for code review as a pull request and once approved, merge upstream. This guide is intended for internal developers with push access to all relevant repos.

You should understand rebasing and squashing. For a very good explanation on rebasing and squashing with pull requests see How to Rebase a Pull Request. Also worth reading is the Hacker's Guide to Git.

Setup

leimao

Git/Github step-by-step Workflow

Step-by-step guide for creating a feature or bugfix branch, submit it for code review as a pull request and once approved, merge upstream. This guide is intended for internal developers with push access to all relevant repos.

You should understand rebasing and squashing. For a very good explanation on rebasing and squashing with pull requests see How to Rebase a Pull Request. Also worth reading is the Hacker's Guide to Git.

Setup

leimao

"""
    Speeds up estimator.predict by preventing it from reloading the graph on each call to predict.
    It does this by creating a python generator to keep the predict call open.
    Usage: Just warp your estimator in a FastPredict. i.e.
    classifier = FastPredict(learn.Estimator(model_fn=model_params.model_fn, model_dir=model_params.model_dir), my_input_fn)
    This version supports tf 1.4 and above and can be used by pre-made Estimators like tf.estimator.DNNClassifier. 
    Author: Marc Stogaitis
    # https://github.com/marcsto/rl/blob/master/src/fast_predict2.py
 """
import tensorflow as tf