Cacher is the code snippet organizer for pro developers

{
  "search_model_queries": {
    "type": "search_model_queries",
    "queries": [
      "how to find best electricity deal Fort Worth Texas",
      "electricity provider Fort Worth how to choose",
      "Texas electricity market Fort Worth compare plans"
    ]
  },
  "search_results": [
    {
      "domain": "comparepower.com",
      "url": "https://comparepower.com/electricity-rates/texas/fort-worth-electricity-rates-energy-plans/",
      "title": "Compare Fort Worth Electricity Rates & Plans 

⁤

/**
 * @param {number[]} target
 * @return {number}
 */
var minNumberOperations = function(target) {
    // Initialize the total number of operations needed
    let operations = 0;

    // Loop through the target array
    for (let i = 0; i < target.length; i++) {
        if (i === 0) {
            // For the first element, we need to perform 'target[0]' operations
            operations += target[0];
        } else {
            // For subsequent elements, we only need to add the difference
   

# Find all failed authentication attempts
fields @timestamp, userIdentity.principalId, errorCode, errorMessage
| filter errorCode like /Unauthorized|Denied|Failed/
| sort @timestamp desc

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


# === 1. Multimodal Pedagogical Encoder (HIPLE Core) ===
class MultimodalPedagogicalEncoder(nn.Module):
    """Processes multimodal teaching signals and student responses."""
    def __init__(self, input_dim=256, hidden_dim=512, num_layers=2):
        super().__init__()
        self.attn = nn.MultiheadAttention(hidden_dim, num_heads=4, batch_first=True)
        self.rnn = nn.GRU(hidden_dim, hidden_dim, num_layers=num

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


class MultimodalEncoder(nn.Module):
    """Encodes strain, vibration, and temperature sensor data using dynamic spatial attention."""
    def __init__(self, input_dim=128, hidden_dim=256):
        super().__init__()
        self.ln = nn.LayerNorm(input_dim)
        self.proj_q = nn.Linear(input_dim, hidden_dim)
        self.proj_k = nn.Linear(input_dim, hidden_dim)
        self.proj_v = nn.Linear(input_dim, hidden_d

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


class VolunteeringEncoder(nn.Module):
    """Encodes volunteering activities into contextual latent representations."""
    def __init__(self, input_dim, hidden_dim=256):
        super().__init__()
        self.fc = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

    def forward(self

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


class LSTMEncoder(nn.Module):
    """Encodes sequential input using stacked LSTM layers."""
    def __init__(self, input_dim, hidden_dim, num_layers=2):
        super().__init__()
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True, dropout=0.2)
    
    def forward(self, x):
        out, _ = self.lstm(x)
        return out


class MultiScaleConv(nn.Module):
    """Extracts temporal 

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


class StructuredEncoder(nn.Module):
    """
    Feedforward encoder for structured data (tabular, KPIs, etc.)
    """
    def __init__(self, input_dim, hidden_dim=256):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.BatchNorm1d(hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU()
      

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

class HierarchicalBlock(nn.Module):
    """
    Hierarchical block that refines feature representations
    through multiple nonlinear transformations.
    """
    def __init__(self, in_dim, hidden_dim, num_layers=3):
        super().__init__()
        self.layers = nn.ModuleList()
        for i in range(num_layers):
            self.layers.append(nn.Sequential(
                nn.Linear(in_dim if i == 0 else hidde

<!DOCTYPE html>
<html lang="en-us">

<head>
    <base href="https://cdn.jsdelivr.net/gh/genizy/web-port@main/baldi-plus/">
    <meta charset="utf-8">
    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
    <title>Unity WebGL Player | Baldi's Basics Plus</title>
    <style>
        html,
        body {
            margin: 0;
            padding: 0;
            border: 0;
            height: 100%;
            width: 100%;
            overflow: hidden;
         

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


# ----------------------------------------------------
# 1. Multimodal Encoder: CNN + Temporal Attention
# ----------------------------------------------------
class MultiScaleEncoder(nn.Module):
    """
    Extracts spatial features using multi-scale dilated convolutions.
    """
    def __init__(self, in_channels=3, base_channels=64):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, base_ch

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

# ------------------------------
# SceneGraph-Informed Neural Feedback Engine (SNiFE)
# ------------------------------

class SceneGraphEmbedding(nn.Module):
    """
    Scene graph embedding and conditioning module.
    Encodes geometric, semantic, and aesthetic object attributes.
    """
    def __init__(self, input_dim=128, hidden_dim=256):
        super().__init__()
        self.geo_encoder = nn.Linear(input_d

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


class DynamicEdgeEncoder(nn.Module):
    """
    Dynamic Edge Encoding Module
    Learns time-varying adjacency matrices for IoT graphs
    using latency, reliability, and contextual metadata.
    """
    def __init__(self, hidden_dim, threshold=0.2):
        super().__init__()
        self.edge_fc = nn.Linear(hidden_dim, hidden_dim)
        self.att_proj = nn.Linear(hidden_dim, 1)
        self.threshold = thresh

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

class SymbolGuidedAttention(nn.Module):
    """
    Symbol-Guided Multi-Head Attention with symbolic bias vectors.
    Implements the symbolic-aware attention mechanism from NSCT.
    """
    def __init__(self, d_model, n_heads):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.scale = (d_model // n_heads) ** 0.5
        self.WQ = nn.Linear(d_model, d_model)