How to use the clustergrammer2.clustergrammer_fun.enrichr_functions.add_enrichr_cats function in clustergrammer2

* ENCODE_Histone_Modifications_2015
      * Disease_Perturbations_from_GEO_up
      * Disease_Perturbations_from_GEO_down
      * GO_Molecular_Function_2015
      * GO_Biological_Process_2015
      * GO_Cellular_Component_2015
      * Reactome_2016
      * KEGG_2016
      * MGI_Mammalian_Phenotype_Level_4
      * LINCS_L1000_Chem_Pert_up
      * LINCS_L1000_Chem_Pert_down

    '''

    df = self.export_df()
    df, bar_info = enr_fun.add_enrichr_cats(df, axis, lib)
    self.load_df(df)

    self.dat['enrichrgram_lib'] = lib
    self.dat['row_cat_bars'] = bar_info

def make_clust(net, dist_type='cosine', run_clustering=True, dendro=True,
                          requested_views=['pct_row_sum', 'N_row_sum'],
                          linkage_type='average', sim_mat=False, filter_sim=0.0,
                          calc_cat_pval=False, sim_mat_views=['N_row_sum'],
                          run_enrichr=None, enrichrgram=None):
  '''
  This will perform hierarchical clustering
  '''

  # threshold = 0.0001
  # df = run_filter.df_filter_row_sum(df, threshold)
  # df = run_filter.df_filter_col_sum(df, threshold)

  if run_enrichr is not None:
    df = net.dat_to_df()
    df = enr_fun.add_enrichr_cats(df, 'row', run_enrichr)
    define_cat_colors = True
    net.df_to_dat(df, define_cat_colors=True)

  inst_dm = calc_clust.cluster_row_and_col(net, dist_type=dist_type,
                                linkage_type=linkage_type,
                                run_clustering=run_clustering,
                                dendro=dendro, ignore_cat=False,
                                calc_cat_pval=calc_cat_pval)

  which_sim = []

  if sim_mat == True:
    which_sim = ['row', 'col']
  elif sim_mat == 'row':
    which_sim = ['row']
  elif sim_mat == 'col':