How to use the multiqc.plots.table.plot function in multiqc

}
            header["max"] = {
                'title': 'Max',
                'description': 'Maximum coverage',
                'shared_key': 'coverage'
            }
            header["std"] = {
                'title': 'Std. Dev.',
                'description': 'Coverage standard deviation',
                'shared_key': 'coverage'
            }
            config = {'namespace': 'deepTools plotCoverage'}
            self.add_section(
                name = "Coverage metrics",
                anchor = "deeptools_coverage_metrics",
                plot = table.plot(self.deeptools_plotCoverageStdout, header, config)
            )

        if len(self.deeptools_plotCoverageOutRawCounts) > 0:
            config = {
                'id': 'deeptools_coverage_metrics_plot',
                'title': 'deepTools: Coverage distribution',
                'xlab': 'Coverage',
                'ylab': 'Fraction of bases sampled'
            }
            self.add_section(
                name = "Coverage distribution",
                anchor = "deeptools_coverage_distribution",
                description = "The fraction of bases with a given number of read/fragment coverage",
                plot = linegraph.plot(self.deeptools_plotCoverageOutRawCounts, config)
            )

pheader['mu_all_botgc'] = {'title':'Mu.low.gc','description':'Bottom Decile in GC Content'}
        pheader['cv_all'] = {'title':'CV.gnm','description':'Whole Genome'}
        pheader['cv_all_topgc'] = {'title':'CV.high.gc','description':'Top Decile in GC Content'}
        pheader['cv_all_botgc'] = {'title':'CV.low.gc','description':'Bottom Decile in GC Content'}
        pheader['mu_cpg'] = {'title':'CG.Mu.gnm','description':'All CpGs'}
        pheader['mu_cpg_topgc'] = {'title':'CG.Mu.high.gc','description':'Top Decile in GC Content'}
        pheader['mu_cpg_botgc'] = {'title':'CG.Mu.low.gc','description':'Bottom Decile in GC Content'}
        pheader['cv_cpg'] = {'title':'CG.CV.gnm','description':'All CpGs'}
        pheader['cv_cpg_topgc'] = {'title':'CG.CV.high.gc','description':'Top Decile in GC Content'}
        pheader['cv_cpg_botgc'] = {'title':'CG.CV.low.gc','description':'Bottom Decile in GC Content'}

        self.add_section(
            name = 'Sequencing Depth',
            anchor = 'biscuit-seq-depth',
            description = "This plot shows sequence depth mean and uniformity measured in Coefficient of Variation (mu/sigma), mapQ>40 only. CG.* shows the depth on CpG only. GC contents were measured on 100bp non-overlapping windows.",
            plot = table.plot(pd, pheader))

### Write the report
        self.write_data_file(reports, 'multiqc_supernova')
        config_table = {
            'id': 'supernova_table',
            'namespace': 'supernova'
        }
        self.add_section (
            name = 'Assembly statistics',
            anchor = 'supernova-table',
            description = 'Statistics gathered from the summary report(s) of Supernova. Note! ' \
                    'There are more columns available but they are hidden by default.',
            helptext = 'As a bare minimum these numbers are generated from the file report.txt, ' \
                    'found in the folder `sampleID/outs/`. If available the stats in the report ' \
                    'file will be superseded by the higher precision numbers found in the file ' \
                    '`sampleID/outs/assembly/stats/summary.json`',
            plot = table.plot(reports, self.headers, config_table)
        )

        # N50 barcharts
        n50_cats = [{'Scaff N50': {'name': 'Scaffold N50', 'color': '#66c2a5'}},
                {'Contig N50': {'name': 'Contig N50', 'color': '#fc8d62'}},
                {'Edge N50': {'name': 'Edge N50', 'color': '#8da0cb'}},
                {'Phase N50': {'name': 'Phase block N50', 'color': '#e78ac3'}}
        ]
        config_n50 = {
                'id': 'supernova_n50',
                'title': 'Supernova: N50 statistics',
                'ylab': 'Scaffold N50',
                'cpswitch': False,
                'data_labels': ['Scaffold N50', 'Contig N50', 'Edge N50', 'Phase block N50']
        }
        self.add_section (

'suffix': '%',
            'scale': 'RdYlGn'
        }
        table_config = {
            'namespace': 'interop',
            'id': 'interop-runmetrics-detail-table',
            'table_title': 'Sequencing Lane Statistics',
            'col1_header': 'Run - Lane - Read',
        }

        tdata = {}
        for s_name in data:
            for key in data[s_name]['details']:
                tdata["{} - {}".format(s_name,key)]=data[s_name]['details'][key]

        return table.plot(tdata, headers, table_config)