code: def plot_umap(
      self,
      data,
      show_plot=True,
      output_filename=None,
      clusters=None,
      title="UMAP Clustering",
  ):
      slicer_cmap = mpl.colors.ListedColormap(
          ["red", "green", "blue", "cyan"]
      )
  
      fig = plt.figure()
      ax = fig.add_subplot(111)
      ax.scatter(data[:, 0], data[:, 1], c=clusters, cmap=slicer_cmap)
      plt.title(title, fontsize=18)
  
      if output_filename is not None:
          plt.savefig(
              self.current_umap_plot_filename, bbox_inches="tight"
          )
  
      if show_plot:
          plt.show()
  
      plt.clf()
  
imports: import numpy
  import tables
  import os
  import umap
  import matplotlib.pyplot
  import matplotlib
  import scipy.ndimage
  from sklearn.cluster import KMeans
  from deepneuro.outputs.output import Output
  from deepneuro.utilities.util import add_parameter, docker_print, replace_suffix
  from deepneuro.utilities.conversion import save_data
label_desc: Use image node_id 5 for calling the PatchInterpretability obj's underlying member method code with example usage: obj.plot_umap(data, show_plot, output_filename, clusters, title) without return types