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function tssh --description="SSH to a machine using its Tailscale IP." --argument-names=target_host
    # Find the `tailscale` executable
    set cmd '/Applications/Tailscale.app/Contents/MacOS/Tailscale'
    if not test -x $cmd
        set cmd (which tailscale)
        if test $status != 0
            set_color red
            printf "ERROR "
            set_color normal
            echo "Could not find Tailscale binary" >&2

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"""Token bucket algorithm implementation and example application/consumer."""
import argparse
import asyncio
import datetime
import random

from loguru import logger


class TokenBucket:

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import pika

#: Name of the Celery events exchange
EXCHANGE_NAME = "celeryev"
#: Arbitrary name for the transient queue this script will create and consume from
QUEUE_NAME = "pikatest"
#: Global to hold our channel object in
channel = None

# Step #2

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import typing as t

from fastapi import Depends, FastAPI, Header, HTTPException
from fastapi.security.http import HTTPAuthorizationCredentials, HTTPBearer
from pydantic import BaseModel
from starlette import status


app = FastAPI()
# Placeholder for a database containing valid token values

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import shelve
import string
import time


class MeasurementDB(object):
    r"""Database for storing measurements, with export to LaTeX macros.

    Example usage:

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#!/bin/bash
# Run commands in the Docker container with a particular UID and GID.
# The idea is to run the container like
#   docker run -i \
#     -v `pwd`:/work \
#     -e LOCAL_USER_ID=`id -u $USER` \
#     -e LOCAL_GROUP_ID=`id -g $USER` \
#     image-name bash
# where the -e flags pass the env vars to the container, which are read by this script.
# By setting copying this script to the container and setting it to the

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def pdf_probabilities(xs, dependents, yields, pdfs):
    """Return an array of normalised PDF probabilities.

    For a list of N yields, each y_i, and a list of N PDFs f_i, the probability
    for the ith PDF at the (possibly vector) point x is defined as

        p_{i} = \frac{N_{i}f_{i}}{\sum_{i}^{N} N_{i}f_{i}}

    Keyword arguments:
    xs -- List of values at which to evaluate the PDFs

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