text: import torch
  import torch.nn.functional as F
  
  torch.manual_seed(1)
  
  z = torch.FloatTensor([1, 2, 3])
  
  hypothesis = F.softmax(z, dim=0)
  
  z = torch.rand(3, 5, requires_grad=True)
  hypothesis = F.softmax(z, dim=1)
  
  y = torch.randint(5, (3,)).long()
  
  y_one_hot = torch.zeros_like(hypothesis)
  y_one_hot.scatter_(1, y.unsqueeze(1), 1)
  
  cost = (y_one_hot * -torch.log(hypothesis)).sum(dim=1).mean()
  
  '''inserted code'''
  import sys
  sys.path.append("/data")
  from scripts.utils.torch_utils import TorchScheduler
  scheduler = TorchScheduler(name="Study.torch20_softmax")
  '''inserted code'''
  
  while True:
      z = torch.autograd.Variable(z, requires_grad=True)
      hypothesis = F.softmax(z, dim=1)
      y = torch.randint(5, (3,)).long()
      y_one_hot = torch.zeros_like(hypothesis)
      y_one_hot.scatter_(1, y.unsqueeze(1), 1)
      loss = (y_one_hot * -torch.log(hypothesis)).sum(dim=1).mean()
  
      '''inserted code'''
      scheduler.loss_checker(loss)
      scheduler.check_time()
      '''inserted code'''
  
  torch.log(F.softmax(z, dim=1))
  
  F.log_softmax(z, dim=1)
  
  (y_one_hot * -torch.log(F.softmax(z, dim=1))).sum(dim=1).mean()
  
  (y_one_hot * -F.log_softmax(z, dim=1)).sum(dim=1).mean()
  
  F.nll_loss(F.log_softmax(z, dim=1), y)
  
  F.cross_entropy(z, y)
  