Noland Chaliha yearofthewhopper

sudo xcode-select -s /Applications/Xcode.app/Contents/Developer

	function convertMarkdownToPlainText() {
	// Get the active document and body
	var body = DocumentApp.getActiveDocument().getBody();

	// Retrieve the text from the document
	var text = body.getText();

	// Replace common Markdown patterns with plain text equivalents
	text = text.replace(/__([^_]+)__/g, '$1'); // bold
	text = text.replace(/\\([^\]+)\\*/g, '$1'); // bold

	import Scene from 'Scene';
	import FaceTracking from 'FaceTracking';
	import Animation from 'Animation';

	/*
	Model structure:
	- Model
	- skeleton (null)
	- Armatures (null objs)
	- Petal & pollen (meshes)

	#!/usr/bin/python
	# -- coding: utf-8 --

	from __future__ import with_statement
	import sys
	import struct
	import StringIO

	//// I adapted this very slightly from the sparkar docs to use with world template as is https://sparkar.facebook.com/ar-studio/learn/tutorials/scripting?utm_campaign=welcome_screen_learn_more&utm_medium=learn&utm_offering=spark-ar&utm_product=spark-ar&utm_source=product#scaling-the-boombox-with-pinch-gestures
	const Scene = require('Scene');
	const TouchGestures = require('TouchGestures');

	const sceneRoot = Scene.root;

	Promise.all([
	sceneRoot.findFirst('planeTracker0'),
	sceneRoot.findFirst('dragHere')
	])

	const Scene = require('Scene');
	const Patches = require('Patches');
	const Reactive = require('Reactive');
	export const Diagnostics = require('Diagnostics');


	(async function () {

	const onCameraRecord = await Patches.outputs.getBoolean('cameraRec');
	// const onScreenTap = await Patches.outputs.getPulse('onScreenTap');

	#define PI 3.14159265359
	#define TWO_PI 6.28318530718

	// 2D Shapes

	float line(in vec2 st, in vec2 p1, in vec2 p2, in float thickness) {
	float a = abs(distance(p1, st));
	float b = abs(distance(p2, st));
	float c = abs(distance(p1, p2));

	using namespace std;
	// @param[default=#00EFFFF0] progressColor
	// @param[default=#FF2FFFF0] timeSpentColor
	// @param[default=5.0,min=0.0,max=60.0] Duration
	void mainImage(vec4 progressColor, vec4 timeSpentColor,float Duration,out vec4 fragColor )
	{
	float Time = getTime();
	vec2 Resolution = getRenderTargetSize();
	vec2 fragCoord = fragment(getVertexTexCoord()) * Resolution;
	float dur = Duration;

	model = Model()
	model.eval()
	random_input = torch.randn(1, 3, 512, 512, dtype=torch.float32)
	input_names = ["image"]
	output_names = ["outputImage"]
	torch.onnx.export(model.netG, random_input, './model.onnx', verbose=False,
	input_names=input_names, output_names=output_names,
	opset_version=11)

	#import <gradients>
	#import <sdf>
	using namespace std;
	// Entry point of the shader code asset //
	// @param[default=#00FFFFFF] colorl
	// @param[default=#0000C0FF] color2
	// @param[default=5.0,min=4.0,max=9.0] armCount
	// @return color
	vec4 main(vec4 colorl, vec4 color2, float armCount){
	float time = sin(getTime()/0.9);