Lei Mao leimao

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

	"""
	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

	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:

	#include <iostream>

	/*
	* Modified from
	* https://www.geeksforgeeks.org/friend-class-function-cpp/
	*/

	class A;
	class B;

	#include <iostream>

	// https://stackoverflow.com/questions/860339/difference-between-private-public-and-protected-inheritance

	class A
	{
	public:
	int x = 1;
	// Constructor
	A() {}

	#include <iostream>

	// Since C++ 11
	template<typename T>
	using func_t = T (*) (T, T);

	template <typename T>
	__device__ T add_func (T x, T y)
	{
	return x + y;

	import numpy as np

	stride_MACRO = 2
	batch_MACRO = 2
	C_MACRO = 4
	H_MACRO = 6
	W_MACRO = 6


	"""

	#include <iostream>

	// more complicated typedef
	typedef int int_t, *intp_t, (&fp)(int, float), arr_t[10];

	int my_int_func(int x, float y)
	{
	return x;
	}

	# Check blog details:
	# https://yerevann.github.io/2016/06/26/combining-cnn-and-rnn-for-spoken-language-identification/
	# TensorFlow sample code
	# http://nicholaspropes.com/entries/general/updated-cnn-rnn-tensorflow-example-code

	import tensorflow as tf
	import numpy as np
	import matplotlib.pyplot as plt
	import math


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