code: def aggregate_patch_data(self, hdf5, output_npy_file):
      patch_data = hdf5.root.patches
  
      if self.aggregation_method == "flatten":
          patch_data = np.reshape(patch_data, (patch_data.shape[0], -1))
      elif self.aggregation_method == "average":
          if self.iterative_aggregation:
              new_patch_data = None
              for i in range(patch_data.shape[-1]):
                  print(i)
                  patch_data_slice = patch_data[..., i]
                  mean_slice = np.mean(
                      patch_data_slice,
                      axis=range(1, len(patch_data_slice.shape)),
                  )
                  print(mean_slice.shape)
                  if new_patch_data is None:
                      new_patch_data = mean_slice
                  else:
                      new_patch_data = np.concatenate(
                          (new_patch_data, mean_slice), axis=1
                      )
              patch_data = new_patch_data
          else:
              patch_data = np.mean(
                  patch_data,
                  axis=tuple(range(1, len(patch_data.shape) - 1)),
              )
      else:
          raise NotImplementedError
  
      np.save(output_npy_file, patch_data)
  
      return patch_data
  
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 6 for calling the PatchInterpretability obj's underlying member method code with example usage: obj.aggregate_patch_data(hdf5, output_npy_file) and returns: patch_data