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/**
 * @param {character[][]} board
 * @return {void} Do not return anything, modify board in-place instead.
 */
var solveSudoku = function(board) {
    const SIZE = 9;

    // Track used digits in rows, columns, and 3x3 boxes
    const row = Array.from({ length: SIZE }, () => Array(SIZE).fill(false));
    const col = Array.from({ length: SIZE }, () => Array(SIZE).fill(false));
    const box = Array.from({ length: SIZE }, () => Array(SIZE).fill(false));

    // Initialize tracking arrays based o

/* works in one direction, if both direction is needed, we need to use 
@starting-style for second direction, @see: https://www.youtube.com/shorts/xVt8Foa2u7I (Kevin Powell)


/* toggle - two 2 , starting-style needed */
/* https://jsbin.com/sokazavera/edit?html,css,output */
.txt {
	background-color: #eee;
	padding: 20px;
	margin-top: 15px;
	border-radius: 4px;
	border: 1px solid #ccc;
	display: none;
	opacity: 0;

	transition-property: all;
	transition-duration: 1s;
	transition-behavior: allow

/**
 * Validates a partially filled 9x9 Sudoku board.
 * Only filled cells are checked for rule violations.
 *
 * @param {character[][]} board - 2D array representing the Sudoku board
 * @return {boolean} - true if the board is valid, false otherwise
 */
var isValidSudoku = function(board) {
    // Set to track seen digits in rows, columns, and sub-boxes
    const seen = new Set();

    // Iterate over each cell in the 9x9 grid
    for (let row = 0; row < 9; row++) {
        for (let col = 0; co

```python
# model.py
# Minimal, self-contained PyTorch implementation of a MIDE+FORTA-style model.
# Focus: clean structure and reasonable defaults, easy to extend for research.

from typing import Dict, Optional, Tuple
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


# ----------------------------
# Small building blocks
# ----------------------------

class ConvBNAct(nn.Module):
    """Conv -> BatchNorm -> GELU"""
    def __init__(self, in_ch

```python
from dataclasses import dataclass
from typing import Optional, Literal

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange


# -----------------------------
# Configuration
# -----------------------------
@dataclass
class HACSIConfig:
    # Input feature dims [B, T, D]
    d_vision: int
    d_text: int
    d_physio: int

    # Shared model dim after projection
    d_model: int = 512

    # Fusion strategy: 'cross_att

```python
import torch
import torch.nn as nn
import torch.optim as optim

class OncoMindNetwork(nn.Module):
    def __init__(self, input_dim, latent_dim, num_modalities):
        super(OncoMindNetwork, self).__init__()
        # Define the components for your OncoMind Network
        self.transformer_branch = nn.TransformerEncoderLayer(d_model=latent_dim, nhead=8)
        self.conv_branch = nn.Conv2d(input_dim, latent_dim, kernel_size=3, stride=1)
        self.fc1 = nn.Linear(latent

```python
from __future__ import annotations
from typing import Optional, Dict, Any, Tuple

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


class GRUContextEncoder(nn.Module):
    """
    Contextual Encoding + Temporal Propagation
    - Encodes E_t (environmental inputs) with a GRU to produce hidden states h_t.
    Mirrors Eqs. (6)–(10) in a compact, standard GRU form.
    """
    def __init__(self, env_dim: int, hidden_dim: int, num_layers: int = 1, bidir: 

```python
"""
RiskSqueezeNet (minimal PyTorch reference)
------------------------------------------

A compact model for clinical risk forecasting on temporal EHR features.

Core blocks:
- Bidirectional GRU stack (hierarchical temporal encoding)
- Squeeze-and-Excitation (SE) channel gating
- Multi-Head Self-Attention for global temporal context
- Horizon-aware decoding: per-horizon classification heads (1..H)
- Optional contrastive projection head (for representation learning)

I

---

# model.py

```python
import math
from typing import Optional, Dict
import torch
import torch.nn as nn
import torch.nn.functional as F


class TemporalGating(nn.Module):
    """
    Exponential decay gating: gamma_t = exp(-rho * delta_t)
    Multiplies feature embeddings elementwise (Eq. 7–8).
    """
    def __init__(self, d_model: int):
        super().__init__()
        # One learnable decay per channel (broadcasted across time)
        self.rho = nn.Parameter(torch

"""
model.py — A small, configurable PyTorch model scaffold.

Usage:
    from model import Model
    cfg = {"task": "classification", "backbone": "conv", "in_channels": 3, "num_classes": 10}
    model = Model.from_config(cfg)
    y = model(x)

Design goals:
- Minimal deps (just PyTorch)
- Config-driven
- Two backbones (MLP/Conv) + two heads (classification/regression)
- Small but production-friendly utilities
"""

from typing import Dict, Any, Optional, Tuple
import math
import

---

# model.py

```python
import math
from typing import Optional, Tuple

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


class PositionalEncoding(nn.Module):
    """
    Standard sinusoidal positional encoding.
    """
    def __init__(self, d_model: int, max_len: int = 10000):
        super().__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float32).unsqueeze(1)
        div_term = torch.ex

---

# model.py

```python
from typing import Tuple, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models


class GraphAttentionLayer(nn.Module):
    """
    A simple single-head graph attention layer over a set of latent nodes.

    Given node features Z in R^{B x N x D}, it computes attention weights
    alpha_{ij} = softmax_i( (Z_i W_q) · (Z_j W_k) / sqrt(Da) ) and produces
    H_i = sigma( sum_j alpha_{ij} (Z_j W_v) ).

function scrollEffect() {
    var $target = $('.js-target');
    if(!$target.length) return;

    var opt = {
        root: null,
        rootMargin: '0px 0px -20% 0px',
        threshold: 0,
    };

    var observer = new IntersectionObserver(function(entries) {
        $.each(entries, function(__, entry) {
            if(entry.isIntersecting) {
                $(entry.target).addClass('act');
                observer.unobserve(entry.target);
            }
            // else {
            //  

/**
 * @param {number} n
 * @param {number} m
 * @return {number}
 */
var flowerGame = function(n, m) {
    // Count how many odd numbers are in [1, n]
    const oddX = Math.floor(n / 2) + (n % 2);   // e.g., 1, 3, 5...
    const evenX = Math.floor(n / 2);            // e.g., 2, 4, 6...

    // count how many odd numbers are in [1, m]
    const oddY = Math.floor(m / 2) + (m % 2);
    const evenY = Math.floor(m / 2);

    // Valid pairs are:
    // - odd x with even y
    // - even x with odd y
 

async function populaValoresDashBoardAdmin() {
    const inputDashValoresPagos = document.getElementById('dashValoresPagos');
    const waitPopup = apex.widget.waitPopup();

    try {
        const data = await new Promise((resolve, reject) => {
            apex.server.process(
                "SET_TOTAIS_DASHBOARD",
                {
                    x01: $v('P1_DASH_INICIAL'),
                    x02: $v('P1_DASH_FINAL'),
                    x03: $v('P1_DASH_TIPO_SERVICO'),
    

<form method="post" action="inquiry.php" id="js_validation_form">
  <input type=hidden name="action" value="">
  <table class="base_table">
    <tr>
      <th>会社名・お名前<span class="must">必須</span></th>
      <td><input name="name" type="text"></td>
    </tr>
    <tr>
      <th>メールアドレス<span class="must">必須</span></th>
      <td><input name="mailad" type="email"></td>
    </tr>
    <tr>
      <th>電話番号<span class="must">必須</span></th>
      <td><input name="tel" type="tel"></td>
    </t