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// https://www.geeksforgeeks.org/template-specialization-c/

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import os

def split(filehandler, delimiter=',', row_limit=10000, 
    output_name_template='output_%s.csv', output_path='.', keep_headers=True):
    """
    Splits a CSV file into multiple pieces.
    
    A quick bastardization of the Python CSV library.

    Arguments:

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"""
    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

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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

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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

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# 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)

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import module

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

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

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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. 

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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"

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#include "class.h"

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

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