code: def plot_tsne(x, y, npca=50, markersize=10):
      Xlow = PCA(n_components=npca).fit_transform(x)
      Y = manifold.TSNE(n_components=2).fit_transform(Xlow)
      palette = sns.color_palette("Paired", len(np.unique(y)))
      color_dict = {
          l: c for l, c in zip(range(len(np.unique(y))), palette)
      }
      colors = [color_dict[l] for l in y]
      plt.scatter(Y[:, 0], Y[:, 1], markersize, colors, "o")
      plt.show()
  
file_path: robustness/robustness/tools/vis_tools.py
imports: import numpy
  import matplotlib.pyplot
  from sklearn.decomposition import PCA
  from sklearn import manifold
  import seaborn
label_desc: Use image node_id 6 for calling a global function with example usage: plot_tsne(x, y, npca, markersize) without return types