code: def plot_embeddings(embeddings, num_utter_per_speaker):
      embeddings = embeddings[: 10 * num_utter_per_speaker]
      model = umap.UMAP()
      projection = model.fit_transform(embeddings)
      num_speakers = embeddings.shape[0] // num_utter_per_speaker
      ground_truth = np.repeat(
          np.arange(num_speakers), num_utter_per_speaker
      )
      colors = [colormap[i] for i in ground_truth]
  
      fig, ax = plt.subplots(figsize=(16, 10))
      _ = ax.scatter(projection[:, 0], projection[:, 1], c=colors)
      plt.gca().set_aspect("equal", "datalim")
      plt.title("UMAP projection")
      plt.tight_layout()
      plt.savefig("umap")
      return fig
  
imports: import umap
  import numpy
  import matplotlib
  import matplotlib.pyplot
label_desc: Use image node_id 1 for calling a global function with example usage: plot_embeddings(embeddings, num_utter_per_speaker) and returns: fig