code: def mnist_test_tsne_ssvae(name=None, ssvae=None, test_loader=None):
      """
      This is used to generate a t-sne embedding of the ss-vae
      """
      if name is None:
          name = "SS-VAE"
      data = test_loader.dataset.test_data.float()
      mnist_labels = test_loader.dataset.test_labels
      z_loc, z_scale = ssvae.encoder_z([data, mnist_labels])
      plot_tsne(z_loc, mnist_labels, name)
  
imports: import torch
label_desc: Use image node_id 5 for calling a global function with example usage: mnist_test_tsne_ssvae(name, ssvae, test_loader) without return types