Cacher is the code snippet organizer for pro developers

```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchdiffeq import odeint


class TemporalEncoder(nn.Module):
    """BiGRU-based encoder for multimodal temporal data."""
    def __init__(self, input_dim, hidden_dim, latent_dim):
        super().__init__()
        self.bigru = nn.GRU(input_dim, hidden_dim, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(2 * hidden_dim, latent_dim)

    def forward(self, x):
        h, _ = self.big

```python
import torch
import torch.nn as nn
import torch.nn.functional as F


class LatentMultiAgentEncoding(nn.Module):
    """Latent Multi-Agent Encoding (LME) module."""
    def __init__(self, input_dim, latent_dim):
        super().__init__()
        self.encoder = nn.Linear(input_dim, latent_dim)
        self.decoder = nn.Linear(latent_dim, input_dim)

    def forward(self, x):
        z = F.relu(self.encoder(x))
        recon = self.decoder(z)
        return z, recon

```python
import torch
import torch.nn as nn
import torch.nn.functional as F


class TemporalConvEncoder(nn.Module):
    """Temporal convolution + positional encoding for patient trajectories."""
    def __init__(self, input_dim, hidden_dim, num_layers=3, kernel_size=3):
        super().__init__()
        self.input_fc = nn.Linear(input_dim, hidden_dim)
        self.convs = nn.ModuleList([
            nn.Conv1d(hidden_dim, hidden_dim, kernel_size, padding="same", dilation=2**i)
    

```python
import torch
import torch.nn as nn
import torch.nn.functional as F


class TemporalEncoder(nn.Module):
    """GRU-based temporal encoder for patient clinical sequences."""
    def __init__(self, input_dim, hidden_dim):
        super(TemporalEncoder, self).__init__()
        self.fc_in = nn.Linear(input_dim, hidden_dim)
        self.gru = nn.GRU(hidden_dim, hidden_dim, batch_first=True)

    def forward(self, x):
        # x: [batch, seq_len, input_dim]
        x = F.relu

```python
import torch
import torch.nn as nn
import torch.nn.functional as F


class MotionAwareEncoding(nn.Module):
    """Embed pose, velocity, and acceleration into a unified token."""
    def __init__(self, input_dim, hidden_dim):
        super(MotionAwareEncoding, self).__init__()
        self.fc = nn.Linear(input_dim * 3, hidden_dim)

    def forward(self, pose, velocity, acceleration):
        # Concatenate [x, v, a] -> motion-aware token
        z = torch.cat([pose, velocit

### 1. 必要な情報

- **総データ数**: 全部で何件のデータがあるか（例：100 件）
- **1 ページあたりの表示件数**: 1 ページに何件表示するか（例：3 件）
- **現在のページ番号**: 今何ページ目を見ているか（例：5 ページ目）

### 2. 計算で求められる情報

```javascript
/* 総ページ数 = 総データ数 ÷ 1ページあたりの件数（切り上げ） */
const totalPages = Math.ceil(totalItems / itemsPerPage);
// 例: Math.ceil(100 / 3) = Math.ceil(33.333...) = 34ページ

/* 表示開始位置 = (現在のページ - 1) × 1ページあたりの件数 */
const startIndex = (currentPage - 1) * itemsPerPage;
// 例: 5ページ目なら (5 - 1) × 3 = 12（13番目のデータから表示）

/* 表示終了位置 = 開始位置 + 1ページあたりの件数（ただし総データ数を超えな

import { NextResponse } from "next/server";
import type { NextRequest } from "next/server";

export function middleware(request: NextRequest) {
  /* 
  src/をエイリアスにしている場合、middleware.tsを配置する場所はプロジェクトルートではなくsrc/直下にする必要がある
   */
  const requestHeaders = new Headers(request.headers);
  requestHeaders.set("x-pathname", request.nextUrl.pathname);

  return NextResponse.next({
    request: {
      headers: requestHeaders,
    },
  });
}

export const config = {
  matcher: "/((?!api|_next/static|_next/im

/**
 * @param {string} text
 * @param {string} brokenLetters
 * @return {number}
 */
var canBeTypedWords = function(text, brokenLetters) {
    // Step 1: Split the input text into individual words
    const words = text.split(" ");

    // Step 2: Convert brokenLetters into a Set for fast lookup
    const brokenSet = new Set(brokenLetters);

    // Step 3: Initialize a counter for typeable words
    let count = 0;

    // Step 4: Loop through each word in the text
    for (let word of words) {
 

Add-Type -AssemblyName System.Drawing |
$width = 300 |
$height = 200 |
$bitmap = New-Object System.Drawing.Bitmap $width, $height |
$graphics = [System.Drawing.Graphics]::FromImage($bitmap) |
$graphics.Clear([System.Drawing.Color]::White) |
$bitmap.Save("C:\blank.png", [System.Drawing.Imaging.ImageFormat]::Png) |
$graphics.Dispose() |
$bitmap.Dispose()

Dans le monde digital actuel, l’intelligence artificielle occupe une place de plus en plus centrale. Parmi les outils qui transforment notre manière de travailler, d’apprendre et de communiquer, ChatGPT Gratuit s’impose comme une solution incontournable. Accessible en ligne, sans abonnement coûteux, il offre aux particuliers comme aux professionnels une nouvelle façon d’interagir avec la technologie.

Qu’est-ce que ChatGPT Gratuit ?

ChatGPT Gratuit est un modèle de langage avancé, capable de gé

$CommandText = @"
SELECT
	[t1].[computer_id]
	, [t1].[name]
    , [t1].[serial_num]
    , [t1].[last_inventory]
	, [GS] = (SELECT TOP 1 UPPER([hostname]) FROM mmsettings)
FROM
	[computer] [t1]
"@

$GSSQLData = @()
$GSServers = Get-GSServers
foreach ($GSServer in $GSServers) {
    $GSSQLData += Get-GSSQLData -Instance "$GSServer\SQLEXPRESS" -Database "eXpress" -CommandText $CommandText
}
$GSSQLDataByGS = $GSSQLData | Sort-Object -Property GS
 
if ( ($GSSQLDataByGS | Measure-Obje

# How to identify serial device tty
[Source](https://superuser.com/a/1095432/393146)

You can check the current tty connection configuration using ```cat```.

```
# cat /proc/tty/driver/serial
serinfo:1.0 driver revision:
0: uart:16550A port:000003F8 irq:4 tx:0 rx:0
1: uart:16550A port:000002F8 irq:3 tx:111780 rx:1321 RTS|DTR|DSR
2: uart:unknown port:000003E8 irq:4
3: uart:unknown port:000002E8 irq:3
```

* A line with uart:unknown means: nothing detected (and likely not existent)
* CTS, DSR, (D

# VPC et Subnets
## Image Globale
Un VPC est comme un étage entier loué dans l'immense gratte-ciel AWS.
Cet étage est complètement isolé des autres locataires, personne ne pouvant y entrer ou y sortir sans autorisation.

## Subnets
Un étage vide n'est pas très utile. On va vouloir y aménager des pièces pour différentes fonctions.
C'est là qu'internviennent les **subnets** (ou **sous-réseaux**).
Un subnet est simplement une subdivision d'un VPC, un peu comme une ou plusieurs pièces de l'étage.

#

#!/bin/bash
# setup-brew-system-user.sh - Complete production-ready setup for _brew system user

set -e  # Exit on any error
set -u  # Treat unset variables as error

# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color

# Logging functions
log_info() { echo -e "${BLUE}[INFO]${NC} $1"; }
log_success() { echo -e "${GREEN}[SUCCESS]${NC} $1"; }
log_warning() { echo -e "${YELLOW}[WARNING]${NC} $1"; }
log_error() { echo -e "${RED}[ERROR

/**
 * @param {string[]} wordlist
 * @param {string[]} queries
 * @return {string[]}
 */
var spellchecker = function(wordlist, queries) {
    // Helper function to normalize vowels in a word
    const normalizeVowels = (word) => {
        return word.toLowerCase().replace(/[aeiou]/g, '*');
    };

    // Step 1: Build lookup structures
    const exactWords = new Set(wordlist); // For exact match
    const caseInsensitiveMap = new Map(); // For case-insensitive match
    const vowelErrorMap = new

# ===============================
# Secrets for MariaDB and MaxScale
# ===============================
apiVersion: v1
kind: Secret
metadata:
  name: mariadb-root-secret
  namespace: mariadb-ha
stringData:
  password: aaaaaaPDaA8fKDv

# ===============================
# MySQL Configuration (ConfigMap)
# ===============================

---
apiVersion: v1
kind: ConfigMap
metadata:
  name: mariadb-config
  namespace: mariadb-ha
data:
  my.cnf: |
    [mariadb]
    skip-name-resolve
    
    max_allo