keon

"use client";
import { useGoogleLogin } from "@react-oauth/google";
import { Passport } from "@0xpass/passport";
import { useCallback, useState } from "react";
import OTPInput from "./components/OTPInput";

export default function Home() {
  const [email, setEmail] = useState("");
  const [otp, setOtp] = useState("");
  const passport = new Passport();

keon

"use client";
import { useGoogleLogin } from "@react-oauth/google";
import { Passport } from "@0xpass/passport";
import { useCallback, useState } from "react";
import OTPInput from "./components/OTPInput";

export default function Home() {
  const [email, setEmail] = useState("");
  const [otp, setOtp] = useState("");
  const passport = new Passport();

keon

import { ChevronDown, Loader2 } from "lucide-react";
import { useEffect, useState } from "react";
import { useConnectionStatus } from "../../hooks/use-connection-status";
import { useEmojiAvatar } from "../../utils/avatars";
import { AsyncImage } from "../common/async-image";
import { PassComponent } from "../common/pass-component";
import { Button } from "../primitives/button";
import { useChains } from "../providers/chain-context";
import { ConnectButtonRenderer } from "./renderer";

keon

{
  "letters": [
    {
      "key": "q"
    },
    {
      "key": "w"
    },
    {
      "key": "e"

keon

var data = [ 
{ 
"answer_content": "2322323", 
"user_id": 49, 
"user_name": "soemthing" 
}, 
{ 
"answer_content": "22222", 
"user_id": 50, 
"user_name": "soemthing" 

keon

var pagenation = function(current, total){
	var list = [];
	var pageLimit = 5;
	var upperLimit, lowerLimit;
	var currentPage = lowerLimit = upperLimit = Math.min(current, total);

	for (var b = 1; b < pageLimit && b < total;) {
	    if (lowerLimit > 1 ) {
	        lowerLimit--; b++; 
	    }

keon

# -*- coding: utf-8 -*-
import random
import gym
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import RMSprop

EPISODES = 5000

keon

# coding: utf-8
"""
Seq2Seq (Encoder-Decoder) Model
this model is the basic encoder decoder model without attention mechanism.
author: Keon Kim
"""

import numpy as np
import torch as th
import torch.nn as nn

keon

x = torch.randn(10)
print(x)
y, ind = torch.sort(x, 0)
print("y", y)
print("ind", ind)
unsorted = y.new(*y.size())
unsorted.scatter_(0, ind, y)
print("unsorted:", unsorted)
print((x - unsorted).abs().max())

keon