code: def pad_data(self, data, pad_dimensions):
      # Maybe more effecient than np.pad? Created for testing a different purpose.
  
      for idx, width in enumerate(pad_dimensions):
          pad_block_1, pad_block_2 = list(data.shape), list(data.shape)
          pad_block_1[idx] = width[0]
          pad_block_2[idx] = width[1]
          data = np.concatenate(
              (np.zeros(pad_block_1), data, np.zeros(pad_block_2)),
              axis=idx,
          )
  
      return 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 12 for calling the PatchInterpretability obj's underlying member method code with example usage: obj.pad_data(data, pad_dimensions) and returns: data