code: def plot_correlated_genes(
              self,
              name,
              n_genes=5,
              number_of_features=1000,
              **kwargs):
          
          all_gene_names = np.array(list(self.adata.var_names))
          if((all_gene_names == name).sum() == 0):
              print(
                  "Gene not found in the filtered dataset. Note that genes "
                  "are case sensitive.")
              return
          sds = self.corr_bin_genes(
              input_gene=name,
              number_of_features=number_of_features)
          if (n_genes + 1 > sds.size):
              x = sds.size
          else:
              x = n_genes + 1
  
          for i in range(1, x):
              self.show_gene_expression(sds[i], **kwargs)
          return sds[1:]
docs: Plots gene expression patterns correlated with the input gene.
  
          Parameters
          ----------
          name - string
              The name of the gene with respect to which correlated gene
              expression patterns will be displayed.
  
          n_genes - int, optional, default 5
              The number of top ranked correlated genes to display.
  
          **kwargs -
              All keyword arguments in 'show_gene_expression' and 'scatter'
              are eligible.