code: def cluster_patch_data(self, input_data):
      if self.open_hdf5_file is not None:
          self.open_hdf5_file.close()
  
      open_hdf5 = tables.open_file(self.current_patch_filename, "r")
      output_npy_file = replace_suffix(
          self.current_aggregate_patch_filename,
          "",
          "_" + self.aggregation_method,
      )
  
      # Load and aggregate data for analysis.
      if (
          not os.path.exists(output_npy_file)
          or self.overwrite_aggregate
      ):
          patch_data = self.aggregate_patch_data(
              open_hdf5, output_npy_file
          )
      else:
          patch_data = np.load(output_npy_file)
  
      print(patch_data.shape)
      print(input_data.shape)
  
      # Calculate Features and Clusters
      if self.baseline_mean_intensity:
          umap_features = patch_data
      else:
          if (
              not os.path.exists(self.current_umap_feature_output)
              or self.overwrite_features
          ):
              umap_features = umap.UMAP(
                  n_neighbors=30, min_dist=0.0, verbose=True
              ).fit_transform(patch_data)
              np.save(self.current_umap_feature_output, umap_features)
          else:
              umap_features = np.load(self.current_umap_feature_output)
  
      if (
          not os.path.exists(self.current_umap_cluster_output)
          or self.overwrite_clusters
      ):
          k_clusters = KMeans(n_clusters=self.cluster_num).fit_predict(
              umap_features
          )
          np.save(self.current_umap_cluster_output, k_clusters)
      else:
          k_clusters = np.load(self.current_umap_cluster_output)
  
      # Plot UMAP and Save Output
      if not self.baseline_mean_intensity:
          if (
              not os.path.exists(self.current_umap_plot_filename)
              or self.overwrite_plot
          ):
              self.plot_umap(
                  umap_features,
                  clusters=k_clusters,
                  show_plot=self.show_umap_plot,
                  output_filename=self.current_umap_plot_filename,
              )
  
      # Map Back to Original Data
      corners = open_hdf5.root.corners
      input_data = input_data[0, ..., 0]
      output_array = np.zeros_like(input_data)
      print(output_array.shape)
      print(corners.shape)
      for idx, coordinate in enumerate(corners):
          # print(coordinate)
          output_array[
              int(coordinate[0]), int(coordinate[1]), int(coordinate[2])
          ] = (k_clusters[idx] + 1)
      padded_points = ndimage.maximum_filter(output_array, 3)
      padded_points[output_array != 0] = output_array[output_array != 0]
  
      save_data(padded_points, self.current_label_filename)
  
      return
  
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 4 for calling the PatchInterpretability obj's underlying member method code with example usage: obj.cluster_patch_data(input_data) without return types