Tony363

Submitting Veo 3 video task to Vertex AI
Endpoint: https://us-central1-aiplatform.googleapis.com/v1/projects/story-board-467506/locations/us-central1/publishers/google/models/veo-3.0-fast-generate-001:predict
Request payload: {
  "instances": [
    {
      "image": "https://36fa157391d7.ngrok-free.app/assets/storyboard/scene_0_image_0_imagen-4.png",
      "prompt": "A sleeping hero, Zero, is awakened by Ciel in a desolate future, his memories fragmented but his resolve to fight clear."
    }
  ],
  "parameters": {

Tony363

/**
 * Simplified Phaser.js game template
 * References modular components instead of embedding entire code
 */
const PHASER_TEMPLATE_MINIMAL = `
// Import game framework modules (pre-loaded in game environment)
const { AUDIO_ASSETS } = window.GameModules.AudioAssets;
const { AudioManager } = window.GameModules.AudioManager;
const { CanvasEffectsManager } = window.GameModules.CanvasEffects;

Tony363

import numpy as np

def find_consecutive_non_zeros(arr):
    non_zero_indices = np.nonzero(arr)[0]
    
    if len(non_zero_indices) < 2:
        return []
    
    results = []
    i = 0

Tony363

def generate_image(unet, autoencoder, scheduler, condition, num_inference_steps=50, guidance_scale=7.5):
    # Set models to evaluation mode
    unet.eval()
    autoencoder.eval()

    # Prepare the condition
    if condition.dim() == 1:
        condition = condition.unsqueeze(0)  # Add batch dimension
    if condition.dim() == 2:
        condition = condition.unsqueeze(1)  # Add sequence length dimension

Tony363

import pandas as pd
import os
import numpy as np
from glob import glob
from sklearn.preprocessing import OneHotEncoder

# Path to TSV file and image directory
tsv_path = "participants.tsv" 
image_dir = "data"                  

Tony363

'''
1. Define a Kernel:
Design a kernel function (or a set of coefficients) that will “spread”
each element of your 14×1 sequence over a larger spatial extent.
For example, you might choose an exponentially decaying or Gaussian kernel 
that determines how much influence each element has over nearby locations.

2. Construct a Toeplitz Matrix:
Build a Toeplitz matrix where the first column is your (possibly zero‑padded) 
14‑element vector and the first row is defined by your kernel. This yields 

Tony363

def main():
    # Read input from input.txt
    with open("input.txt", "r") as fin:
        # First line: highway length K and maximum allowed distance L
        line = fin.readline().strip()
        if not line:
            return
        K, L = map(int, line.split())
        
        # Second line: number of off-ramps (not used directly)

Tony363

def main():
    # Read input
    with open("input.txt", "r") as fin:
        # First line: highway length K and maximum allowed distance L
        first_line = fin.readline().strip()
        if not first_line:
            return
        K, L = map(int, first_line.split())
        
        # Second line: number of off-ramps (we read it but won't rely on it)

Tony363

import bisect


def read_input() -> tuple:
    with open("input.txt", "r") as fin:
        # First line: highway length K and maximum allowed distance L
        first_line = fin.readline().strip()
        if not first_line:
            return
        K, L = map(int, first_line.split())

Tony363

class Solution:
    def deleteDuplicates(self, head: Optional[ListNode]) -> Optional[ListNode]:
        """
        Removes all nodes that have duplicate numbers, leaving only distinct numbers from the original list.
        
        :param head: The head of the sorted linked list.
        :return: The head of the list after removing duplicates.
        """
        # Initialize a dummy node to handle edge cases gracefully
        dummy = ListNode(0)