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ALGORITHM DijkstraNFLStadiums(stadiums, distances, startStadium, endStadium)
INPUT:
    - stadiums: list of all stadium names
    - distances: matrix of distances between stadiums
    - startStadium: starting stadium name
    - endStadium: destination stadium name
OUTPUT:
    - shortest path as list of stadium names
    - total distance in miles

BEGIN
    // Initialize data structures
    distanceMap = empty map
    previousStadium = empty map
    unvisitedStadiums = set of all stadiums
    vis

This script conditionally loads an external JavaScript file only when certain UI elements (header/footer) are missing, helping avoid duplicate or conflicting scripts on pages that already include them.

```// Creates and injects a script tag into the page
const initialize = () => {
  const script = document.createElement("script");
  script.id = "footer-header-script";           // Unique ID to prevent duplicate injection
  script.type = "text/javascript";
  script.src = "https://www.example.com

function ConvertFrom-ByteArray {
    [CmdletBinding()]
    param (
        [Alias('appid')]
        [Parameter(Mandatory = $true, Position = 0, ValueFromPipeline = $true, ValueFromPipelineByPropertyName = $true)]
        [byte[]]
        $byteArray
    )
    
    begin {}

    process {
        try {
            [System.Text.Encoding]::UTF8.GetString($byteArray)
        }
        catch {
            throw $_
        }
    }

    end {}
}

/**
 * @param {number[]} nums
 * @param {number} value
 * @return {number}
 */
var findSmallestInteger = function(nums, value) {
    // Step 1: Create a frequency map to count how many times each remainder appears
    const freq = new Map();

    for (let num of nums) {
        // Normalize the remainder to always be non-negative
        let mod = ((num % value) + value) % value;

        // Count how many times each remainder appears
        freq.set(mod, (freq.get(mod) || 0) + 1);
    }

    /

PLUGINS

• https://www.kadencewp.com/kadence-blocks/
• https://www.kadencewp.com/kadence-blocks/

# model.py
# ChildLangNet: Interaction Pattern Recognition for Early Childhood Language
# PyTorch >= 1.10

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


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

class MLP(nn.Module):
    def __init__(self, in_dim: int, hidden: int, out_dim: int, p: float = 0.1):
        super().__init__()
        self.net = nn.Sequential(
            nn.Li

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

# Define the Vocational Curriculum Alignment Network (VCAN)
class VCAN(nn.Module):
    def __init__(self, curriculum_dim, enterprise_dim, hidden_dim, output_dim):
        super(VCAN, self).__init__()

        # Convolutional layer to process curriculum and enterprise data
        self.curriculum_conv = nn.Conv1d(curriculum_dim, hidden_dim, kernel_size=3, padding=1)
        self.enterprise_conv = nn.Conv1d(enterprise_d

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

# Define the Temporal Skill Progression Network (TSPNet)
class TSPNet(nn.Module):
    def __init__(self, skill_dim, hidden_dim, output_dim):
        super(TSPNet, self).__init__()
        
        # Recurrent neural network (LSTM) to model temporal dependencies in skill progression
        self.lstm = nn.LSTM(skill_dim, hidden_dim, batch_first=True)
        
        # Attention mechanism to focus on the most relevant

# model.py
# TourFusionNet: Multi-Source Data Fusion for Sports Tourism Preference Prediction
# Implements: modality encoders, hierarchical fusion (self & cross attention),
#             graph propagation, and Adaptive Preference Integration Strategy (APIS).
# PyTorch >= 1.10 recommended.

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


# -----------------------------
# Utility blocks
# ----------------------------

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

# Define the Harmonic Scene Integration Network (HSIN)
class HSIN(nn.Module):
    def __init__(self, music_dim, scene_dim, embedding_dim, output_dim):
        super(HSIN, self).__init__()
        
        # Define the multi-modal encoder for music and scene data
        self.music_encoder = nn.Sequential(
            nn.Linear(music_dim, embedding_dim),
            nn.ReLU(),
            nn.Dropout(0.5)
        )

### Commands / Description
Command | Description
---------|-----------
**Search** |
`/{earch_term}` | searches for `{search_term}` in the document
`?{search_term}` } | searches for `{search_term}` in the document
`CNTRL - D` | resets the cursor
**Substitute** | place cursor on the line 
`:s/old/new/g` | every instance of `old` replaced with `new` if `g` or global is used
`:%s/old/new/gc` | `gc` means with prompt, search and replace WHILE typing
**Execute Terminal Commands** | 
`:!{terminal comma

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

# Define the Behavioral Recognition-Driven Quality Assessment Network (BRQAN)
class BRQAN(nn.Module):
    def __init__(self, input_dim, feature_dim, output_dim):
        super(BRQAN, self).__init__()
        
        # Define the multimodal encoder for feature extraction
        self.conv1 = nn.Conv2d(input_dim, 64, kernel_size=3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, pa

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

# Define the Cultural Embedding Transmission Network (CETNet)
class CETNet(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(CETNet, self).__init__()
        
        # Define layers for encoding the motif and cultural memory
        self.encoder = nn.LSTM(input_dim, hidden_dim, batch_first=True)
        self.memory_embedding = nn.Linear(hidden_dim, hidden_dim)  # Cultural memory upd

# models/structural_attention.py
from __future__ import annotations

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


class StructuralAttentionConv(nn.Module):
    r"""
    Structural Attention message passing (single head) inspired by the paper's
    formulations (attention over neighbors with separate W1/W2 and vector 'a').​:contentReference[oaicite:3]{index=3}

    For a directed graph G=(V,E), we compute for edge u->v:
     

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

class MultiModalAttention(nn.Module):
    def __init__(self, input_dims, attention_dims):
        super(MultiModalAttention, self).__init__()
        
        self.attention_layers = nn.ModuleList([
            nn.Linear(input_dim, attention_dims) for input_dim in input_dims
        ])
        self.attention_weights = nn.Parameter(torch.ones(len(input_dims)))

    def forward(self, inputs):
        attention_scores

# models/hape.py
from __future__ import annotations

import math
from typing import Optional, Tuple, Dict

import torch
from torch import nn, Tensor
import torch.nn.functional as F


class SinusoidalPositionalEncoding(nn.Module):
    """
    Classic transformer-style fixed positional encoding.

    Args:
        dim: feature dimension
        max_len: maximum sequence length supported
    """
    def __init__(self, dim: int, max_len: int = 10_000):
        super().__init__()
        pe = torch.z