code: def remove_empty_patches(self, input_data, corners_list):
      corner_selections = []
  
      for corner_idx, corner in enumerate(corners_list):
          output_slice = [slice(None)] * input_data.ndim  # Weird
          for idx, dim in enumerate(self.patch_dimensions):
              output_slice[dim] = slice(
                  corner[idx] - self.input_patch_shape[dim] // 2,
                  corner[idx] + self.input_patch_shape[dim] // 2,
                  1,
              )
  
          corner_selections += [np.any(input_data[tuple(output_slice)])]
  
      return corners_list[corner_selections]
  
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 13 for calling the PatchInterpretability obj's underlying member method code with example usage: obj.remove_empty_patches(input_data, corners_list) and returns: corners_list