Cacher is the code snippet organizer for pro developers

python -m venv venv
source venv/bin/activate

/**
 * @param {number[]} skill
 * @param {number[]} mana
 * @return {number}
 */
var minTime = function(skill, mana) {
    let m = mana.length;       // Number of potions
    let n = skill.length;      // Number of wizards

    // done[i] represents the time when wizard i is ready to start the next potion
    // We use n+1 to simplify indexing (done[n] will hold the final result)
    let done = new Array(n + 1).fill(0);

    // Loop through each potion in order
    for (let j = 0; j < m; j++) {

//after clonning backend ios project:

swift package resolve

/**
 * AEO AI Visibility Booster - from Imran Siddiq - https://learn.websquadron.co.uk
 */

if ( ! defined('ABSPATH') ) exit;

class AIV_Clarity_Ultra {
    const OPTION = 'aiv_ultra_settings';
    const NONCE  = 'aiv_ultra_nonce';

public function __construct() {
    // 1) Disable WordPress core sitemaps entirely (/wp-sitemap.xml and friends)
    add_filter('wp_sitemaps_enabled', '__return_false');

    // 2) Kill canonical redirects for our virtual endpoints so WP (or plugins)
 

```python
# model.py
# Training Load Perception Neural Network (TLPNN)
# Training Load Perception Mechanism (TLPM)
# Inspired by He et al. (2023), IEEE Access

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

# -----------------------
# Multimodal Encoders
# -----------------------
class ModalityEncoder(nn.Module):
    """Encoder for each modality (video, sensor, etc.)"""
    def __init__(self, in_dim, latent_dim):
        super().__init__()
        self.fc = 

```python
# model.py
# Adaptive Multimodal Attention Network (AMAN)
# Adaptive Multimodal Attention Strategy (AMAS)
# Inspired by Hao, Zhao, Qiao (2023) IEEE Access

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


# -----------------------
# Modality-specific Encoders
# -----------------------
class ModalityEncoder(nn.Module):
    """Simple encoder for each modality (text, audio, visual)."""
    def __init__(self, in_dim, latent_dim):
        super().__init_

```python
# model.py
# Reference skeleton for SensorFusion-SportsHealth
# Inspired by Hui Wang & Wanxin Du (2023), IEEE Access

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

# -----------------------
# Multimodal Encoders
# -----------------------

class SensorEncoder(nn.Module):
    """Encodes a single sensor modality."""
    def __init__(self, in_dim, latent_dim):
        super().__init__()
        self.fc = nn.Sequential(
            nn.Linear(in_dim, l

```python
# model.py
# Reference implementation for HarmonyNet-style recommender:
# - Collaborative filtering (matrix factorization)
# - Multimodal fusion (audio/text/image)
# - Lightweight graph propagation over item nodes
# - Optional rule-based reasoning for interpretability
#
# This is a scaffold you can extend with your real data pipeline.

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


# 

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

class DFBM(nn.Module):
    """
    Dynamic Fitness Behavior Model
    Combines CNN (spatial) + RNN (temporal) + Attention
    """
    def __init__(self, input_dim, hidden_dim=64, num_classes=5):
        super().__init__()
        self.conv1 = nn.Conv1d(input_dim, hidden_dim, kernel_size=3, padding=1)
        self.rnn = nn.GRU(hidden_dim, hidden_dim, batch_first=True)
        self.attn = nn.Linear(hidden_d

```python
import numpy as np

class HIM_Agent:
    def __init__(self, state_dim, action_dim, alpha=0.1, beta=0.2, gamma=0.9):
        """
        Harmonious Interaction Model (HIM) agent
        :param state_dim: dimension of state vector
        :param action_dim: number of actions
        """
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.alpha = alpha  # engagement weight
        self.beta = beta    # performance weight
        self.gamma = 

import numpy as np

class DynamicPathGenerationModel:
    def __init__(self, decay_rate=0.1, alpha=0.5, beta=0.7, theta=(0.5, 0.3, 0.2)):
        """
        Initialize model parameters
        :param decay_rate: λ for temporal decay
        :param alpha: weight for attribute relevance
        :param beta: weight for event significance
        :param theta: coefficients for utility function
        """
        self.decay_rate = decay_rate
        self.alpha = alpha
        self.beta

```python
# model.py
# CRVG + KDAM implementation (PyTorch)
# Author: Weiwei Yi (paper); reference code by ChatGPT
# Note: This is a simplified prototype

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

# -------------------------
# Relation-specific Graph Attention
# -------------------------
class RelationalGATLayer(nn.Module):
    def __init__(self, in_dim, out_dim, num_relations):
        super().__init__()
        self.relation_weights = nn.ModuleList([nn

```python
# model.py
# VitalWatch-PTRE-DCAS: PyTorch reference implementation
# Core components:
# 1) Decay-aware temporal encoding (per-signal) with GRUCell
# 2) Graph-based relational modeling with attention-weighted adjacency
# 3) Contextual fusion via MultiheadAttention (Transformer-style)
# 4) DCAS head: signal-wise attention, exponential memory, domain-adaptive threshold
# 5) Optional Monte-Carlo dropout for uncertainty

from typing import Optional, Dict
import torch
import tor

```python
# model.py
# EthosGraph + VirtueFlow implementation
# Based on Feng Xianjing (2023), IEEE Access

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

# -----------------------
# EthosGraph
# -----------------------

class EthosGraph(nn.Module):
    def __init__(self, in_dim=128, hidden_dim=256, embed_dim=64):
        super().__init__()
        self.fc1 = nn.Linear(in_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, embed_dim)

    def forward

```python
# model.py
# LAREN (Latent Risk Equilibrium Network) + ARDIS (Anticipatory Risk-Driven Intervention Strategy)

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

# --------------------------
# LAREN Components
# --------------------------

class LAREN(nn.Module):
    def __init__(self, state_dim=128, action_dim=32, latent_dim=64):
        super().__init__()
        # Risk Encoder
        self.encoder = nn.Sequential(
            nn.Linear(state_dim + a

```python
# model.py
# MusicaNovaNet + Cognitone Calibration
# Reference PyTorch implementation based on Si Zhang's paper (2023)

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

# -------------------------------
# MusicaNovaNet Components
# -------------------------------

class MultimodalEncoder(nn.Module):
    def __init__(self, input_dim=128, hidden_dim=256):
        super().__init__()
        self.gru_task = nn.GRU(input_dim, hidden_dim, batch_first=True)