code: def plot_embedding(emb, y, y_labels):
      # plot a 2D feature map embedding
      x_min, x_max = np.min(emb, 0), np.max(emb, 0)
      emb = (emb - x_min) / (x_max - x_min)
  
      NC = len(y_labels)
      markers = [".", "+", "x", "|", "_", "*", "o"]
  
      fig = plt.figure()
      fig.set_size_inches(6, 6)
  
      for c in range(NC):
          i = y == c
          plt.scatter(
              emb[i, 0], emb[i, 1], marker=markers[c], label=y_labels[c]
          )
  
      plt.xticks([]), plt.yticks([])
      plt.legend()
      plt.tight_layout()
  
imports: import matplotlib.pyplot
  import numpy
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  import seglearn
label_desc: Use image node_id 1 for calling a global function with example usage: plot_embedding(emb, y, y_labels) without return types