text: # demo.py
  
  import torch
  import torchvision.utils as vutils
  import numpy as np
  import torchvision.models as models
  from torchvision import datasets
  from tensorboardX import SummaryWriter
  
  resnet18 = models.resnet18(False)
  writer = SummaryWriter()
  sample_rate = 44100
  freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
  
  for n_iter in range(100):
  
      dummy_s1 = torch.rand(1)
      dummy_s2 = torch.rand(1)
      # data grouping by `slash`
      writer.add_scalar('data/scalar1', dummy_s1[0], n_iter)
      writer.add_scalar('data/scalar2', dummy_s2[0], n_iter)
  
      writer.add_scalars('data/scalar_group', {'xsinx': n_iter * np.sin(n_iter),
                                               'xcosx': n_iter * np.cos(n_iter),
                                               'arctanx': np.arctan(n_iter)}, n_iter)
  
      dummy_img = torch.rand(32, 3, 64, 64)  # output from network
      if n_iter % 10 == 0:
          x = vutils.make_grid(dummy_img, normalize=True, scale_each=True)
          writer.add_image('Image', x, n_iter)
  
          dummy_audio = torch.zeros(sample_rate * 2)
          for i in range(x.size(0)):
              # amplitude of sound should in [-1, 1]
              dummy_audio[i] = np.cos(freqs[n_iter // 10] * np.pi * float(i) / float(sample_rate))
          writer.add_audio('myAudio', dummy_audio, n_iter, sample_rate=sample_rate)
  
          writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)
  
          for name, param in resnet18.named_parameters():
              writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter)
  
          # needs tensorboard 0.4RC or later
          writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter)
  
  dataset = datasets.MNIST('mnist', train=False, download=True)
  images = dataset.test_data[:100].float()
  label = dataset.test_labels[:100]
  
  features = images.view(100, 784)
  writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))
  
  # export scalar data to JSON for external processing
  writer.export_scalars_to_json("./all_scalars.json")
  writer.close()