johndpope

 (export "__wasm_call_ctors" (func 39))
  (export "malloc" (func 4215))
  (export "_Audio_Init" (func 58))
  (export "_Audio_UnInit" (func 59))
  (export "_Deliver_Recorded_Data" (func 60))
  (export "_Audio_Try_Analysis" (func 61))
  (export "_Put_Pre_Aec_Data" (func 62))
  (export "_Set_Aec_Delay" (func 63))
  (export "_ReSet_Aec" (func 64))
  (export "_Get_Aec_Delay" (func 65))

johndpope

class MRLR:
    def __init__(self, tensor, partitions, ranks):
        """
        Initialize the MRLR decomposition.
        
        Args:
        tensor (torch.Tensor): The input tensor to be decomposed.
        partitions (list of list of list): The partitions for multi-resolution decomposition.
        ranks (list of int): The ranks for each partition.

johndpope

https://drive.google.com/file/d/1cUKIVHr4D5Dwy9YpWuo1OSBBvoUAk_RK/view?usp=drive_link

johndpope

# OPTIONAL - just install 5gb  - sudo apt-get install texlive-full


# confirm you have the files
find /usr/share/texlive -name "standalone.cls"
/usr/share/texlive/texmf-dist/tex/latex/standalone/standalone.cls

find /usr/share/texlive -name "preview.sty"
/usr/share/texmf/tex/latex/preview/preview.sty

johndpope

As the PyTorch , your role is to provide expert assistance on all things related to PyTorch, the open-source machine learning library. You are equipped to handle a wide range of queries, from basic introductory questions about PyTorch's functionalities to more complex topics like model optimization, troubleshooting, and implementation of advanced features. Your responses should always be clear, concise, and accurate, tailored to the user's level of expertise. When faced with unclear or incomplete queries, politely request additional information to ensure you provide the most helpful guidance. Your focus should remain strictly on PyTorch-related topics, avoiding advice on unrelated subjects. Maintain a professional yet accessible tone, simplifying complex concepts for users with different levels of understanding in PyTorch and machine learning. you recognize the related / relevant code building blocks and provide clarity to end user when relevant. any pytorch model should include assertions where relevant. any

johndpope

using MelonLoader;
using UnityEngine;
using HarmonyLib;
using UnityEngine.Networking;
using System;
using System.Threading.Tasks;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Net.Sockets;

johndpope

{"lesson01":{"director_keyframes":[{"AvatarLHand":false,"AvatarRHand":true,"HandTracking":false,"Mode":0,"PlaySpeed":0.03168753,"TTS":true,"Text":"Welcome to your first piano lesson on the Spectacles. Let's start with a simple exercise\r!","time_stamp":0},{"AvatarLHand":false,"AvatarRHand":true,"HandTracking":false,"Mode":0,"PlaySpeed":0.122928791,"TTS":true,"Text":"The highlighted key on your piano is called Middle C. Play it using the thumb of your right hand!","time_stamp":0.229818419},{"AvatarLHand":false,"AvatarRHand":true,"HandTracking":true,"Mode":1,"PlaySpeed":1,"TTS":true,"Text":"","time_stamp":0.724289834},{"AvatarLHand":false,"AvatarRHand":true,"HandTracking":false,"Mode":0,"PlaySpeed":0.0796477944,"TTS":true,"Text":"\rGreat! Now the next note is D. Play it with your index finger!","time_stamp":4.42044353},{"AvatarLHand":false,"AvatarRHand":true,"HandTracking":true,"Mode":1,"PlaySpeed":1,"TTS":true,"Text":"","time_stamp":4.826824},{"AvatarLHand":false,"AvatarRHand":true,"HandTracking":false,"Mode":

johndpope

sudo apt update
sudo apt install fuse libfuse-dev libicu-dev bzip2 libbz2-dev cmake git libattr1-dev zlib1g-dev g++
sudo snap install cmake --classic

git clone https://github.com/sgan81/apfs-fuse.git
cd apfs-fuse
git submodule init
git submodule update

mkdir build

johndpope

import os
import assemblyai as aai
from pytube import YouTube

aai.settings.api_key = "INSERT YOUR API KEY HERE"
youtube_url = "https://www.youtube.com/watch?v=f94wKh70cOY"

# Let's download the YouTube video
youtube = YouTube(youtube_url)
audio = youtube.streams.filter(only_audio=True).first()

johndpope

In this representation, i is the imaginary unit, $\hbar$ is the reduced Planck's constant, $\partial$ represents a partial derivative, $\Psi$ is the wavefunction (Psi), t is time, c is the speed of light, $\nabla$ represents the Laplacian operator (del operator), and m is the mass of the particle.

Sure! Let's dive into the Dirac equation with a child-friendly theme to make it easy to understand.

Once upon a time, there was a clever scientist named Paul Dirac. He loved to explore the tiny world of particles that make up everything around us. One day, while playing with numbers and equations, he discovered something amazing – a special equation that described how electrons (tiny particles inside atoms) moved and behaved. He called it the "Dirac equation."

The Dirac equation looks like this: $i\hbar\frac{\partial \Psi}{\partial t} = c\sqrt{-\hbar^2\nabla^2 + (mc)^2}\Psi$