old_contents: import torch
  from torch import FloatTensor, LongTensor
  from torch.autograd import Variable
  from torch import nn, optim
  from torch.nn import Parameter
  from tqdm import trange
  
  import util, logging, os, psutil
  
  import hyper
  
  logging.basicConfig(filename='memleak.log',level=logging.INFO)
  
  torch.manual_seed(2)
  
  B = 256
  M = 32
  IN = OUT = tuple([M] * 8)
  W, H = len(IN) + len(OUT), 2048
  
  for i in trange(int(10e7)):
  
      x = torch.randn((B, H, W)) * M
      x = x.long().cuda()
  
      x = Variable(x)
  
      x, _ = hyper.flatten_indices(x, IN, OUT)
  
  
new_contents: import torch
  from torch import FloatTensor, LongTensor
  from torch.autograd import Variable
  from torch import nn, optim
  from torch.nn import Parameter
  from tqdm import trange
  
  import util, logging, os, psutil
  
  import hyper
  
  logging.basicConfig(filename='memleak.log',level=logging.INFO)
  
  torch.manual_seed(2)
  
  B = 256
  M = 32
  IN = OUT = tuple([M] * 8)
  W, H = len(IN) + len(OUT), 2048
  
  for i in trange(int(10e7)):
  
      x = torch.randn((B, H, W)) * M
      x = x.long().cuda()
  
      x = Variable(x)
  
      x, _ = hyper.flatten_indices(x, IN, OUT)
  
      if i % 25 == 0:
          logging.info(util.nvidia_smi())
  
  
  
subject: Add logging of memory use