Amir01 AmirMohamadBabaee

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare git@github.com:usi-systems/easytrace.git

	function sharif_login
	{
	curl -d "username=$1&password=$2" -X POST "https://net2.sharif.edu/login" > /dev/null
	curl -s https://net2.sharif.edu/status \| grep -o '<td>.*</td>'
	sleep 1s # To See login status
	}
	function sharif_ip
	{
	# from ping net2.sharif.edu
	# this is written since sometimes DNS server doesn't work!

	from typing import Union, List, Optional

	from collections.abc import Mapping, Sequence

	import torch.utils.data
	from torch.utils.data.dataloader import default_collate
	from torch_geometric.data import Data, HeteroData, Dataset, Batch
	from torch_geometric_temporal.signal import StaticGraphTemporalSignal as SGTS
	from torch_geometric_temporal.signal import DynamicGraphTemporalSignal as DGTS
	from torch_geometric_temporal.signal import DynamicGraphStaticSignal as DGSS

	import requests
	from tqdm import tqdm


	def download(url: str, fname: str, chunk_size=1024):
	resp = requests.get(url, stream=True)
	total = int(resp.headers.get('content-length', 0))
	with open(fname, 'wb') as file, tqdm(
	desc=fname,
	total=total,

	;;; The Y Combinator explained in scheme.
	;;; with credits to:
	;;; https://mvanier.livejournal.com/2897.html
	;;; Status: WIP

	(define fibonacci
	(lambda (n)
	(cond ((= n 0) 0)
	((= n 1) 1)
	(else (+ (fibonacci (- n 1)) (fibonacci (- n 2)))))))

	'''
	"Contrast Limited Adaptive Histogram Equalization"
	by Karel Zuiderveld, karel@cv.ruu.nl
	in "Graphics Gems IV", Academic Press, 1994

	_Author_ -- Siladittya Manna

	The below implementation does not assume that the
	X- and Y image resolutions are an integer multiple
	of the X- and Y sizes of the contextual regions.

	'''
	numpy sinusoid position encoding of Transformer model.

	params:
	n_position(n):number of positions
	d_hid(m): dimension of embedding vector
	padding_idx:set 0 dimension

	return:
	sinusoid_table(n*m):numpy array

	import os
	import yaml
	import logging.config
	import logging
	import coloredlogs

	def setup_logging(default_path='logging.yaml', default_level=logging.INFO, env_key='LOG_CFG'):
	"""
	\| @author: Prathyush SP
	\| Logging Setup

	<!-- Example #1 - no styling -->
	Made with ❤ in Switzerland
	Made with ♥ in Switzerland
	Made with ♡ in Switzerland
	Made with ❤️ in Switzerland
	Made with ♥️ in Switzerland

	<!-- Example #2 - inline-styled ❤ -->
	Made with <span style="color: #e25555;">♥</span> in Switzerland
	Made with <span style="color: #e25555;">&hearts;</span> in Switzerland