BetterProgramming

services:
db:
  image: mongo:latest
  ports:
    - "27017:27017"
  container_name: db
  volumes: shared_database:/data/db
vc_db:
  image: mongo:latest
  volumes: shared_database:/data/db

BetterProgramming

//In bot.js
const token = <"YOUR_SAVED_BOT_TOKEN">; //Token that you saved in step 5 of this tutorial
const {Client, Intents} = require("discord.js");
const client = new Client({
    intents:[
        Intents.FLAGS.GUILDS,
        Intents.FLAGS.GUILD_MESSAGES 
    ]
});
client.on("ready", () =>{

BetterProgramming

#!/usr/bin/env bash
#: Your comments here.
set -o errexit
set -o nounset
set -o pipefail
work_dir=$(dirname "$(readlink --canonicalize-existing "${0}" 2> /dev/null)")
readonly conf_file="${work_dir}/script.conf"
readonly error_reading_conf_file=80
readonly error_parsing_options=81
readonly script_name="${0##*/}"

BetterProgramming

sal = df.loc[:,["salary", "gender"]].groupby(["salary", "gender"]).size().reset_index()
sal.columns = ["Salary", "Gender", "Count"]
fig = px.bar(sal, x='Salary', y='Count', color="Gender", 
             barmode='group', title ="Salary Gender Distribution", 
             height=400, width=2000)
fig.update_traces(marker_line_color='rgb(9,50,100)',
                  marker_line_width=1.5, opacity=0.6)
fig.show()

BetterProgramming

package wsc;
// Depending on wsdl chosen, change this to enterprise or partner
import com.sforce.soap.enterprise.*;
import com.sforce.soap.enterprise.sobject.*;
import com.sforce.ws.*;
public class Main { 
  public static void main(String[] args) {
    ConnectorConfig credentials = new ConnectorConfig();
    credentials.setPassword("YourPassword!apPenDdedSecurityToken);
    credentials.setUsername("yoursalesforceusername@yourdomain.com");

BetterProgramming

def train_epocs(model, epochs=10, lr=0.01, wd=0.0):
    optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=wd)
    model.train()
    for i in range(epochs):
        usernames = torch.LongTensor(train_df.UserId.values)
        game_titles = torch.LongTensor(train_df.TitleId.values)
        ratings = torch.FloatTensor(train_df.Userscore.values)
        y_hat = model(usernames, game_titles)
        loss = F.mse_loss(y_hat, ratings)
        optimizer.zero_grad()  # reset gradient

BetterProgramming

import React, {Component} from 'react';
import{AlertIOS,AppRegistry,Platform,StyleSheet,Text,TouchableOpacity,Button,View,} from 'react-native';
import Video from 'react-native-video';
export default class VideoPlayer extends Component {
static navigationOptions  = ({ navigation }) =>({header:null}),});
constructor(props) {
 super(props);
 this.onLoad = this.onLoad.bind(this); 
 this.onProgress = this.onProgress.bind(this);
 this.onBuffer = this.onBuffer.bind(this);

BetterProgramming

extension NSNotification.Name {
  public static let deviceDidShakeNotification =   NSNotification.Name("MyDeviceDidShakeNotification")
}
extension UIWindow {
  open override func motionEnded(_ motion: UIEvent.EventSubtype, with event: UIEvent?) {
  super.motionEnded(motion, with: event)
  NotificationCenter.default.post(name: .deviceDidShakeNotification, object: event)
}
}
extension View {

BetterProgramming

import { lineString, along, lineDistance } from '@turf/turf';
const OPTIONS = { units: 'feet' };
// 30.1 seconds, the .1 is to allow a buffer for the next set of cords to load
// I know it's not exact, but it's close :)
const STEPS = 30100;


//inside component function
useEffect(() => {
    let arc = [];

BetterProgramming

$ vim web-ns.yaml
apiVersion: v1
kind: Namespace
metadata:
  name: web
  labels:
    tier: web 
$ kubectl apply -f web-ns.yaml
namespace/web created
$ vim middleware-ns.yaml