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func readJson[T any](w http.ResponseWriter, r *http.Request) (T, error) {
    maxBytes := 1_048_576
    r.Body = http.MaxBytesReader(w, r.Body, int64(maxBytes))

    dec := json.NewDecoder(r.Body)
    dec.DisallowUnknownFields()

    var dst T
    if err := dec.Decode(&dst); err != nil {
        return dst, err

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using System;
using System.Collections.Generic;
using System.Linq;

namespace FunctionalExtensions
{
    [Serializable]
    public abstract class ValueObject : IComparable, IComparable<ValueObject>
    {
        private int? _cachedHashCode;

zckkte

# export mysql database
mysqldump -u username -p database_name > data-dump.sql

# Check if a sql script is a legit sql dump file
 head -n 5 prodbck_anon_reduced.sql | grep dump ; echo $?

# import 

mysql -u root -p

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object Console {
  def putStrLn(str: => String) = TIO.effect(println(str))
}

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<?php


class BusinessResult
{
    public const SUCCESS = "success";

    public const FAIL = "fail";

    private string $result;

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function curry(fn) {
    return function curriedFn(...args) {
        if (fn.length !== args.length) {
            return curriedFn.bind(null, ...args);
        }
        return fn.apply(null, args);
    };
}

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-- \/\/\/ DO NOT MODIFY THE FOLLOWING LINES \/\/\/
  
module ReversiAI (State,author,nickname,initial,think) where

import Reversi
import Data.List
import Data.Map (fromList, (!))
import Data.Maybe

-- /\/\/\ DO NOT MODIFY THE PRECEDING LINES /\/\/\

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import os
import math
import random
import shutil 
import numpy as np
import matplotlib.pyplot as plt
from time import time
from functional import seq
from collections import namedtuple
import keras

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from keras.models import Model

layer_outputs = [layer.output for layer in model.layers] 
activation_model = Model(inputs=model.input, outputs=layer_outputs) 
activations = activation_model.predict(X_train[10].reshape(1,28,28,1))

def display_activation(activations, col_size, row_size, act_index):
  activation = activations[act_index]
  activation_index=0
  fig, ax = plt.subplots(row_size, col_size, figsize=(row_size*2.5,col_size*1.5)) 

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import keras
from keras import layers
from keras import optimizers
import matplotlib.pyplot as plt
import numpy as np
from keras.models import load_model
from keras.regularizers import l2
from keras.callbacks import EarlyStopping
from keras.layers import Dropout