Chirag Jain chiragjn

Edit: moved to here https://blog.haicon.moe/posts/logitech-f310-to-work-with-macbook-usb-c-port/

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Short description of this PR, postable to Twitter, < 140 char (You never know, we might)

The Problem (The Why)

The tragic backstory. The troubles the townfolk (users) had to face.

How to configure Bash Completion on Mac for Docker and Docker-Compose

Copied from the official Docker-for-mac documentation (thanks Brett for the updated doc pointer):

Install shell completion

Docker Desktop for Mac comes with scripts to enable completion for the docker, docker-machine, and docker-compose commands. The completion scripts may be found inside Docker.app, in the Contents/Resources/etc/ directory and can be installed both in Bash and Zsh.

Bash

Bash has built-in support for completion To activate completion for Docker commands, these files need to be copied or symlinked to your bash_completion.d/ directory. For example, if you installed bash via Homebrew:

	"""
	Runs a DBus message loop with a callback to receive... pretty much
	anything on a session bus.
	Listen to the Notify event; if it's a message we hate, record it, then
	listen for the return message, and kill that message.

	Tested On:
	- Ubuntu 18.04.5 LTS, CPython 3.6.9 (dbus: 1.2.6, gi: 3.26.1)

	To test, try running the following with and without this script running:

	import socket
	from redis.client import Redis
	from redis.connection import Connection, ConnectionPool
	from redis._compat import iteritems


	class ProxyConnection(Connection):
	def __init__(self, host='localhost', port=6379, db=0, password=None,
	socket_timeout=None, socket_connect_timeout=None,
	socket_keepalive=False, socket_keepalive_options=None,

	#!/usr/bin/env bash
	#
	# Copyright (c) 2016-present, Facebook, Inc.
	# All rights reserved.
	#
	# This source code is licensed under the MIT license found in the
	# LICENSE file in the root directory of this source tree.
	#

	set -e

	from gensim.models import KeyedVectors

	# Load gensim word2vec
	w2v_path = '<Gensim File Path>'
	w2v = KeyedVectors.load_word2vec_format(w2v_path)

	import io

	# Vector file, `\t` seperated the vectors and `\n` seperate the words
	"""

	from keras import backend as K
	from keras.engine import InputSpec
	from keras.engine.topology import Layer
	import numpy as np


	class TemporalMaxPooling(Layer):
	"""
	This pooling layer accepts the temporal sequence output by a recurrent layer
	and performs temporal pooling, looking at only the non-masked portion of the sequence.

	## Weight norm is now added to pytorch as a pre-hook, so use that instead :)

	import torch
	import torch.nn as nn
	from torch.nn import Parameter
	from functools import wraps

	class WeightNorm(nn.Module):
	append_g = '_g'
	append_v = '_v'

	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:
	x (): input
	kernel (): weights
	Returns:
	"""
	if K.backend() == 'tensorflow':