RuolinZheng08

def is_valid_state(state):
    # check if it is a valid solution
    return True

def get_candidates(state):
    return []

def search(state, solutions):
    if is_valid_state(state):
        solutions.append(state.copy())

EdisonLeeeee

import torch
import tensorflow as tf

########## PyTorch Version 1 ################
x = torch.randn(5, 6)
norm_th = x/torch.norm(x, p=2, dim=1, keepdim=True)
norm_th[torch.isnan(norm_th)] = 0 # to avoid nan

########## PyTorch Version 2 ################
norm_th = torch.nn.functional.normalize(x, p=2, dim=1)