code: def load(self, kwargs):
      super(PatchInterpretability, self).load(kwargs)
  
      # Model Parameters
      add_parameter(self, kwargs, "input_channels", None)
      add_parameter(self, kwargs, "channels_dim", None)
      add_parameter(self, kwargs, "output_channels", None)
      add_parameter(self, kwargs, "output_layers", [None])
  
      # Patching Parameters
      add_parameter(self, kwargs, "patch_method", "random")
      add_parameter(self, kwargs, "patch_num", 50000)
      add_parameter(self, kwargs, "mask_condition", 0)
      add_parameter(self, kwargs, "patch_overlaps", 1)
      add_parameter(self, kwargs, "output_patch_shape", None)
      add_parameter(self, kwargs, "check_empty_patch", True)
      add_parameter(self, kwargs, "pad_borders", True)
      add_parameter(self, kwargs, "patch_dimensions", None)
      add_parameter(
          self, kwargs, "output_patch_dimensions", self.patch_dimensions
      )
  
      # Aggregation Parameters
      add_parameter(self, kwargs, "aggregation_method", "flatten")
      # add_parameter(self, kwargs, 'iterative_aggregation', True)
  
      # Umap Parameters
      add_parameter(self, kwargs, "umap_components", 2)
  
      # Clustering Parameters
      add_parameter(self, kwargs, "cluster_individual_case", False)
      add_parameter(self, kwargs, "cluster_num", 10)
  
      # Output Parameters
      add_parameter(self, kwargs, "generate_patches", False)
      add_parameter(self, kwargs, "aggregate_patches", True)
      add_parameter(self, kwargs, "umap_patches", True)
      add_parameter(self, kwargs, "cluster_patches", True)
      add_parameter(self, kwargs, "show_umap_plot", False)
      add_parameter(self, kwargs, "save_projected_output", True)
  
      # Output Naming Paramters
      add_parameter(self, kwargs, "patch_filename", None)
      add_parameter(self, kwargs, "aggregate_patch_filename", None)
      add_parameter(self, kwargs, "features_filename", None)
      add_parameter(self, kwargs, "clusters_filename", None)
      add_parameter(self, kwargs, "umap_plot_filename", None)
      add_parameter(self, kwargs, "data_filename", None)
      add_parameter(self, kwargs, "label_filename", None)
  
      # Overwrite Parameters
      add_parameter(self, kwargs, "overwrite_patches", False)
      add_parameter(self, kwargs, "overwrite_aggregate", True)
      add_parameter(self, kwargs, "overwrite_features", True)
      add_parameter(self, kwargs, "overwrite_clusters", True)
      add_parameter(self, kwargs, "overwrite_plot", True)
      add_parameter(self, kwargs, "overwrite_outputs", True)
  
      # Experimental Parameters
      add_parameter(self, kwargs, "baseline_mean_intensity", False)
  
      self.open_hdf5_file = None
  
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 1 for calling the PatchInterpretability obj's underlying member method code with example usage: obj.load(kwargs) without return types