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# model.py
# PyTorch reference implementation of GTRM
# - Heterogeneity-Aware Encoding (HAE)
# - Cross-Omics Fusion Network (COFN)
# - Mutation–Drug Scoring (MDS)
#
# This code is framework-faithful to the paper while remaining concise and easy to extend.

from dataclasses import dataclass
from typing import Dict, Optional, Tuple

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


# -----------------------------
# Utilities
# -----------------------------

d

# model.py
# PlasmoNet: Hierarchical encoder + Dual-Branch Attention + Graph-Based Reasoning
# with PlasmoAdapt utilities for domain adaptation.
#
# This implementation follows the design described in the uploaded article:
# - Hierarchical Visual Encoding (Sec. 3.3; Fig. 1–2)
# - Dual-Branch Attention (Eq. 13–17)
# - Graph-Based Inference (Eq. 18–21)
# - PlasmoAdapt (prototype alignment, consistency & spectral matching; Eq. 23–34)
#
# Minimal dependencies: torch

from typing import D

# model.py
# Reference PyTorch implementation of SDGN + ACR
# Derived from the uploaded article's method section and equations.
# Minimal dependencies: torch, numpy

from __future__ import annotations
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


# ---------------------------
# Utilities
# ---------------------------

def pairwise_sqdist(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
    """
    Compute squared Euclidea

```python
# model.py
# A compact PyTorch implementation inspired by CGINet + SARM for medical image segmentation
# from: "Deep learning uncertainty quantification method for image segmentation and treatment decision-making in radiation oncology"
# Key components: Semantic Projection Mechanism (SPM), Cross-Concept Interaction Modeling (CCIM),
# Layer & Sequence Alignment (LSA), and SARM attribution refinement / consistency.
#
# NOTE:
# - This is a minimal, readable reference, not a hype

# model.py
# Reference PyTorch implementation of MedStructFormer + KIDL ideas (2D classification)
# Paper reference: "Enhancing Image Classification Reliability in Radiation Oncology via Uncertainty Quantification"
# Key elements: Anatomical Positional Encoding (Eq. 15), Anatomically-Constrained Attention (Eqs. 16–18),
# Multi-scale outputs (Eqs. 20–24), Knowledge-Gated Modulation (Eq. 26), MC-Dropout uncertainty (Eqs. 36–37).

from typing import List, Optional, Tuple
import math
import 

### Replaces all newline characters in input.txt with spaces, outputting a single-line result.

```sh
sed ':a;N;$!ba;s/\n/ /g' input.txt
```

### Change ownership of current directory (recursive)
```sh
# Run with sudo to change ownership
# Set both user and group to the current logged-in user
# Apply recursively (-R) to all files and subdirectories
sudo chown -R "$USER:$USER" "$PWD"
```


### Convert images to PDF and extract images from PDF

```sh
# Install tools
sudo apt install img2pdf popple

### Reset branch to a specific commit
```sh
git reset --hard <commit_id>       # Move local branch to the given commit, discarding changes
git push --force <remote> <branch> # Overwrite remote branch history with local state
```

### Git configuration behind a proxy
```
git config user.name "Your Name"
git config user.email "your.email@example.com"

# Proxy via cntlm
git config http.proxy 'http://127.0.0.1:3128'
git config https.proxy 'http://127.0.0.1:3128'

# Cache credentials for 30 days
git

apex.theme.closeRegion("ALTERA_RECEBEDOR");

document.addEventListener("DOMContentLoaded", () => {
  const footer = document.querySelector(".l-footer");
  const banner = document.querySelector(".floating-banner");

  if (!footer || !banner) return;

  const observer = new IntersectionObserver((entries) => {
    entries.forEach((entry) => {
      if (entry.isIntersecting) {
        // footerが見えたら非表示
        banner.classList.add("is-hidden");
      } else {
        // footerが見えなくなったら表示
        banner.classList.remove("is-hidden");
      }
  

// フローティングバナーの表示制御
document.addEventListener('DOMContentLoaded', function () {
  const fixedEl = document.querySelector('.-fixed');
  const kvEl = document.querySelector('.sectionKv_ttl');
  const ctaEl = document.querySelector('.ctaContainerBottom_block');

  if (!fixedEl || !kvEl || !ctaEl) return;

  const kvHeight = kvEl.offsetHeight;

  function getTriggerPoint() {
    if (window.innerWidth <= 768) {
      return kvHeight * 0.25; // SP
    } else {
      return kvHeight * 0.1; // PC
    }
 

$DCs = [System.DirectoryServices.ActiveDirectory.Domain]::GetCurrentDomain().DomainControllers.Name
$ScriptBlock = { Get-ChildItem -Path Cert:\LocalMachine\My | Select-Object -ExpandProperty EnhancedKeyUsageList }
Invoke-Command -ScriptBlock $ScriptBlock -ComputerName $DCs -ErrorAction SilentlyContinue | Select-Object -Property * -ExcludeProperty RunspaceId | Sort-Object -Property PSComputerName | Where-Object FriendlyName -match 'smart card' | Format-Table -AutoSize

when we open a terminal on Unix machine - the default shell is Bash
to check type:
which $SHELL
#! - shebang

BASH - the Bourne again shell

to activate BASH from kali type:
bash

bash starts with $

BASH is also dynamic interpreted command language that can be used to write scripts
to create a custom script to add to my project create a file that ends with .sh or with no extencion:
touch filename.sh or filename


write command like:
echo "my script"

create variables:
GREET="Rita"

to access th

/**
 * @param {number[][]} points
 * @return {number}
 */
var numberOfPairs = function(points) {
    let count = 0;

    // Step 1: Sort points by x ascending, then y descending
    // This ensures that for any point[i], all point[j] with j < i have x <= x[i]
    points.sort((a, b) => {
        if (a[0] === b[0]) return b[1] - a[1]; // If x is equal, sort by y descending
        return a[0] - b[0]; // Otherwise, sort by x ascending
    });

    // Step 2: Iterate through each point[i] as potenti

# Core scientific stack
numpy>=1.24.0
pandas>=2.0.0
scipy>=1.11.0

# Deep learning framework
torch>=2.0.0
torchvision>=0.15.0
torchaudio>=2.0.0

# Machine learning & utilities
scikit-learn>=1.3.0
networkx>=3.1
tqdm>=4.65.0

# Optimization
cvxpy>=1.3.2

# Data handling
xarray>=2023.1.0
netCDF4>=1.6.4
pyyaml>=6.0
hydra-core>=1.3.2

# Visualization
matplotlib>=3.7.0
seaborn>=0.12.2
plotly>=5.14.0

# Optional: GPU accelerated optimization (only if CUDA is available)
cup

// Check if any QuoteHed row is marked for deletion
var deletedQuote = ds.QuoteHed.FirstOrDefault(r => r.RowMod == "D");

if (deletedQuote != null)
{
    // Throw a business logic exception to prevent deletion
    // This ensures the user cannot delete sales quotes through the UI or service
    throw new BLException("Sales Quote deletion is not allowed.");
}

#include <WiFi.h>
#include <Wire.h>
#include <LiquidCrystal_I2C.h>
#include <DHT.h>
#include <BlynkSimpleEsp32.h>
#include <NTPClient.h>
#include <WiFiUdp.h>