code: def write_patches_to_hdf5(self, patches, corners):
      self.output_storage.append(patches)
      self.corner_storage.append(corners)
  
      # if self.data_collection.source == 'hdf5':
      #     self.casename_storage.append(np.array(self.data_collection.data_groups[self.inputs[0].data_casenames[case_name][0])[np.newaxis][np.newaxis])
      # else:
      #     self.casename_storage.append(np.array(bytes(self.data_collection.current_case, 'utf-8'))[np.newaxis][np.newaxis])
  
      pass
  
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 11 for calling the PatchInterpretability obj's underlying member method code with example usage: obj.write_patches_to_hdf5(patches, corners) without return types