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/**
 * @param {number[][]} grid
 * @return {number[][]}
 */
var sortMatrix = function(grid) {
    const n = grid.length;

    // Helper to extract a matrix starting at (row, col)
    function getMatrix(row, col) {
        const matrix = [];
        while (row < n && col < n) {
            matrix.push(grid[row][col]);
            row++;
            col++;
        }
        return matrix;
    }

    // Helper to write a sorted matrix back into the grid
    function setMatrix(row, col, sorted) {
  

# Disable KVM permanently (so VirtualBox can use AMD-V) and restore it later if you need QEMU/virt-manager again.


## Disable KVM so VirtualBox can use AMD-V 
1. Create a blacklist file for KVM modules:<br> 
`echo -e "blacklist kvm\nblacklist kvm_amd" | sudo tee /etc/modprobe.d/disable-kvm.conf`
2. Rebuild initramfs so the blacklist applies at early boot (needed on Ubuntu/Debian; harmless to run anyway):<br>
`sudo update-initramfs -u`
3. Reboot your system. After reboot, verify that kvm is not 

---

# model.py

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

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


# --------------------------
# Config
# --------------------------

@dataclass
class CEMConfig:
    # Input
    feat_dim: int = 128          # frame feature size F
    # Temporal encoder
    hidden_size: int = 256       # GRU hidden (per direction)
    num_layers: int = 1
    dropout: float = 0.1

---

# model.py

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

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


# -------------------------
# Config
# -------------------------
@dataclass
class TriLanSphereConfig:
    # Text
    vocab_size: int = 32000
    d_model: int = 256
    max_len: int = 128
    n_heads: int = 4
    n_layers_text: int = 2
    dropout: float = 0.1

    # Spatial (objects + v

---

# model.py

```python
"""
AeroVISTA: Trajectory-Level Sightseeing Quality Scoring
-------------------------------------------------------

This module implements a compact PyTorch version of the AeroVISTA model:

- Semantic visual encoding (lightweight CNN by default; pluggable backbone)
- Attention-gated GRU for temporal aggregation
- Geospatial encoder for (x, y, z)
- Landmark Graph Integration (GCN-like)
- Permutation-invariant trajectory pooling
- Utility scoring with 

---

# model.py

```python
from __future__ import annotations
from dataclasses import dataclass
from typing import Optional, List, Dict, Tuple

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


# ----------------------------
# Data containers
# ----------------------------

@dataclass
class Batch:
    z: torch.Tensor                   # (B, T, d_z): multimodal/event features per step
    c0: torch.Tensor                  # (B, d_c): initial competency (s

<div class="speeldata-container">
    {% set pairs = (speeldata|length / 2)|round(0, 'floor') %}

    {% if pairs > 0 %}
        <div class="lines">
            {% for i in 1..pairs %}
                {% set shape_position = i is odd ? 'left' : 'right' %}
                <div class="line-shape">
                    {% include 'partials/svg.twig' with {
                        attr_height:'250',
                        id:'vertical-line-' ~ shape_position,
                        ariaLabel:'Lange

---

# model.py

```python
# model.py
# Minimal SCAN + ACRS (PyTorch)
# MIT License

from __future__ import annotations
import math
from typing import Dict, Optional, Tuple

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


def _pairwise_diff(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
    """
    Compute pairwise differences a[:, :, None, :] - b[:, None, :, :]

    a: (B, N, D)
    b: (B, N, D)
    return: (B, N, N, D)
    """
    return a.u

---

## model.py

```python
# model.py
# A compact PyTorch implementation of NeuroSymNet-style stress detection.
# Components:
# - Physiological encoder: Conv1D + Positional encoding + BiGRU
# - Cycle-phase gating (sinusoidal phase embedding -> sigmoid gate)
# - Cross-channel attention
# - Symbolic temporal abstraction (↑/↓/→)
# - Temporal graph-style propagation (self-attention across time)
# - 3-class classifier head (+ helper ordinal loss and MC-dropout utilities)

from typin

# model.py
# SpineBERT: attention-driven behavioral recognition with biomechanical priors
# Minimal, self-contained PyTorch implementation inspired by the reference design.
# See equations and figures referenced in the accompanying README. :contentReference[oaicite:18]{index=18}

from dataclasses import dataclass
from typing import Optional, Tuple

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


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

# model.py
# Minimal reference implementation of SEEN + RPCS (PyTorch)
# Author: Legend Co., Ltd. (Savannah Banks) — reference implementation
# License: MIT

from __future__ import annotations
from dataclasses import dataclass
from typing import Optional, Tuple

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


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

class MLP(nn.Module):
    def __init__(self, d_in: int, d_hidden: int, d_o

"""
AthlEthosNet (Minimal)
----------------------
A compact PyTorch module demonstrating:
  - Dual-State Representation (moral `mu`, behavior `psi`)
  - Mutually Influential (gated) updates
  - Memory-Guided Behavioral Modeling (attention over moral history)
  - Coherence regularizer (cosine alignment between projected moral & behavior)

Shapes (per forward step)
-------------------------
Inputs:
  o_t:        (B, obs_dim)
  mu_tm1:     (B, moral_dim)
  psi_tm1:    (B, beh_dim)
  

std::string vec_to_string(const std::vector<int>& vec)
{
	return std::accumulate(	std::next(vec.begin()), vec.end(), 
							std::to_string(vec.front()), 
							[](std::string acc, int b) { return std::move(acc) + ',' + std::to_string(b); }
                          );
};

std::cout << vec_to_string( {1,2,3,4} ) << std::endl;

# About me
I'm a junior Python data/AI developer with intermediate level, working on a project for which  I need your 
pedagogical and technical guidance on.


# Project description
I want to build an SQL Agent interact with thanks to a chatbot UI, and leave feedbacks.
The users are French native speakers, asking hence questions in French and being answered in French.
The database contains also data in French language.


# Your task
I want you to act as a senior Python developer spec

# collectstatic command with ignore list

# run manually:
# run from terminal
# manage.py collectstatic -v 2 -i "admin/*" -i "katan/*" -i "rest_framework/*" -i "debug_toolbar/*"
# or from django shell:
# collectstatic -v 2 -i admin/* -i katan/* -i rest_framework/* -i debug_toolbar/*


#!/usr/bin/env python
import os
import sys
from django.core.management import execute_from_command_line

project_name = "Django_project_4"

# Patterns to ignore
IGNORE_PATTERNS = [
    "admin/*",

// pass in the parms required
var addThings = AddItems(true, 4, myList);

// Now it only requires the things
myThings.ForEach(addThings);


// The first set of parms are added to the currying func to return the final func
// item is projected out of the list it will be run in
private Action<Thing> AddItems(bool isTrue, int numberOfThings, List<Thing> list) =>
item => AddItemToList(isTrue, numberOfThings, list, item);



// The original method you want to curry the first three pars