code: def embed_umap(data):
      """data should be on cpu, numpy"""
      embedding = UMAP(
          metric="euclidean",
          n_neighbors=40,
          # angular_rp_forest=True,
          # random_state=torch.initial_seed(),
          transform_seed=torch.initial_seed(),
      )
      return embedding.fit_transform(data)
  
file_path: mmvae/src/vis.py
imports: import matplotlib.colors
  import matplotlib.pyplot
  import numpy
  import pandas
  import seaborn
  import torch
  from matplotlib.lines import Line2D
  from umap import UMAP
label_desc: Use image node_id 2 for calling a global function with example usage: embed_umap(data) and returns: embedding