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// use std::fs::File;
use itertools::Itertools;
use std::io::{self, BufRead, BufReader, BufWriter, Write};

use serde::{Deserialize, Serialize};
// use std::path::Path;

#[derive(Serialize, Deserialize)]
struct Item<'a> {
    id: i64,

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#!/bin/bash 

# Simple pattern for exporting a query from postgres into jsonl using psql \copy command  

pg_url=$POSTGRES_URL
fpath=$FPATH

psql $pg_url > $fpath << EOF 
  \copy(select to_jsonb(r) as jl from (select * from my_table) r ) to stdout;
EOF

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#!/usr/bin/env python 
""" standalone cli script for easy version bumping with pyproject.
"""
import argparse 
import toml
import pathlib
import sys 
import re
import os 
import subprocess

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"""
Assuming you have pyproject.toml we will read in the current version tag into the environment tag: VERSION_TAG
This will be available in your workflow now.

Place this at the root of .github folder

"""
import toml 
import os
import sys

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from typing import Any, List, NamedTuple, Optional
import pydantic 
import datetime
import re

_ym_re = re.compile(r'[0-9]{4}-[0-9]{2}')

class YearMonth(NamedTuple):
    """A custom YearMonth type. Can validate against YYYY-MM, ('YYYY', 'MM') or (int, int). 
    Checks that year and month are valid ISO 8601 ranges.

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""" A high-level API that wraps the sklearn Pipeline interface. If designed well we should be able to use this base class to 
encapsulate elements of jupyter notebook script. A user should subclass and override the specified hook methods of these classes 
to build a workflow for deployment and benchmarking.  
"""
import abc  
#from sklearn.externals.joblib import parallel_backend  warning says to use parallel backend from joblibe directly... 
from joblib import parallel_backend
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV,\
     ParameterGrid, ParameterSampler

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""" Defines a generic interface for composable widgets. Panels are the view layer, Datastores are the model. 

Downloaders should fill the datastores with data. Panels should register observers to implement parts of the API. This could be 
as simple as downloading and displaying data, or calling custom APIs that call things.
"""
import abc

import IPython
import ipywidgets as widgets
import inspect

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{
    "configurations": [
        {
            "name": "Linux",
            "includePath": [
                "${workspaceFolder}/**",
                "~/.conan/**"
            ],
            "defines": [],
            "compilerPath": "/usr/bin/clang",

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from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker, scoped_session

from contextlib import contextmanager

import logging
l = logging.getLogger(__name__)


class DataAccessLayer(object):

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# based on digital ocean for 18.04

$ sudo apt update
$ sudo apt install postgresql postgresql-contrib

# make a new non-postgres user for the system without switching roles
$ sudo -u postgres createuser --interactive
$ sudo -u postgres createdb <your-user-name>

# setup a db password for user