lyger

import math
import random

def dice(num, size):
    return sum(random.randint(1, size) for _ in range(num))
    
    
class Character:
    
    def damage(self, amount):

lyger

let links = document.getElementsByClassName('nav-reverse');
function sleep(ms) {
  return new Promise(resolve => setTimeout(resolve, ms));
}
for (let i=0; i<links.length; i++) {
	let l = links[i]
	if (l.innerText === 'Download' || l.innerText === 'ダウンロード') {
		l.click();
		await sleep(500);
	}

lyger

import torch
import torch.nn as nn
import torch.nn.functional as F

from typing import Tuple, Union

from .autopackingrnn import AutoPackingRNN

FloatType = Union[torch.FloatTensor, torch.cuda.FloatTensor]
LongType = Union[torch.LongTensor, torch.cuda.LongTensor]

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import torch.utils.data as D
from myutils.data import EmbeddingLoader, CompositeConverter, TextDataset, \
    ParallelDataset, ParallelCollate, lowercase, tokenize_with_bos_and_eos

emb_en = EmbeddingLoader('/cl/work/michael-l/multiembed/en.multiCCA.512.embedding', 512)
emb_de = EmbeddingLoader('/cl/work/michael-l/multiembed/de.multiCCA.512.embedding', 512)

converter = CompositeConverter(lowercase, tokenize_with_bos_and_eos)

news_en = emb_en.process_dataset(

lyger

Alos
Amu
Ardol
Argola
Aricheo
Arite
Arlele
Arma
Armara
Armile

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#!/usr/bin/env python3

import _dynet as dy
import numpy as np
from collections import OrderedDict

# End of word token.
EOW = "<EOW>"

lyger

const templates = {
  "Humanoid traits": [
    "with [genericadj+limbadj] [limbs]",
    "with a missing [limb+appendage+face]",
    "with [genericadj+eyeadj] eyes",
    "wearing @[jewelryadj] [jewelry]",
    "with [hairadj] hair",
    "with @[voiceadj] voice"
    ],
  "Mammalian traits": [