How to use the kipoi.metadata.GenomicRanges function in kipoi

def __next__(self):
        ss = next(self.exonGenerator)
        out = {}
        out['inputs'] = {}
        seq = ss.get_seq(self.fasta).upper()
        if self.split_seq:
            seq = self.split(seq, ss.overhang)['exon'][0]
        out['inputs']['input_3'] = seq

        out['metadata'] = {}
        out['metadata']['ranges'] = GenomicRanges(
            ss.chrom,
            ss.Exon_Start,
            ss.Exon_End,
            ss.transcript_id,
            ss.strand)

        return out

# Dnase
        dnase = np.squeeze(self.dnase_extractor([interval], axis=0))[:, np.newaxis]
        dnase[np.isnan(dnase)] = 0  # NA fill
        dnase_rc = dnase[::-1]

        bigwig_list = [seq]
        bigwig_rc_list = [seq_rc]
        mappability = np.squeeze(self.mappability_extractor([interval], axis=0))[:, np.newaxis]
        mappability[np.isnan(mappability)] = 0  # NA fill
        mappability_rc = mappability[::-1]
        bigwig_list.append(mappability)
        bigwig_rc_list.append(mappability_rc)
        bigwig_list.append(dnase)
        bigwig_rc_list.append(dnase_rc)

        ranges = GenomicRanges.from_interval(interval)
        ranges_rc = GenomicRanges.from_interval(interval)
        ranges_rc.strand = "-"

        return {
            "inputs": [
                np.concatenate(bigwig_list, axis=-1),  # stack along the last axis
                np.concatenate(bigwig_rc_list, axis=-1),  # RC version
                np.append(self.meta_feat, gencode_counts)
            ],
            "targets": {},  # No Targets
            "metadata": {
                "ranges": ranges,
                "ranges_rc": ranges_rc
            }

raise ValueError("Expected the interval to be {0} wide. Recieved stop - start = {1}".
                             format(self.SEQ_WIDTH, interval.stop - interval.start))

        if self.targets is not None:
            y = self.targets.iloc[idx].values
        else:
            y = {}

        # Run the fasta extractor
        seq = np.squeeze(self.fasta_extractor([interval]), axis=0)
        seq = np.expand_dims(np.swapaxes(seq, 1, 0), axis=1)
        return {
            "inputs": seq,
            "targets": y,
            "metadata": {
                "ranges": GenomicRanges.from_interval(interval)
            }

# automatically resize the sequence to cerat
            interval = resize_interval(
                interval, self.auto_resize_len, anchor='center')

        # QUESTION: @kromme - why to we need max_seq_len?
        # if self.max_seq_len is not None:
        #     assert interval.stop - interval.start <= self.max_seq_len

        # Run the fasta extractor and transform if necessary
        seq = self.fasta_extractors.extract(interval)

        return {
            "inputs": np.array(seq),
            "targets": labels,
            "metadata": {
                "ranges": GenomicRanges(interval.chrom, interval.start, interval.stop, str(idx))
            }

def __getitem__(self, idx):
        if self.fasta_extractor is None:
            self.fasta_extractor = FastaExtractor(self.fasta_file)
        interval = self.bt[idx]

        if interval.stop - interval.start != self.SEQ_WIDTH:
            raise ValueError("Expected the interval to be {0} wide. Recieved stop - start = {1}".
                             format(self.SEQ_WIDTH, interval.stop - interval.start))

        # Run the fasta extractor
        seq = np.squeeze(self.fasta_extractor([interval]), axis=0)
        return {
            "inputs": {"dna": seq},
            "metadata": {
                "ranges": GenomicRanges.from_interval(interval)
            }

format(self.SEQ_WIDTH, interval.stop - interval.start))

        if self.targets is not None:
            y = self.targets.iloc[idx].values
        else:
            y = {}

        # Run the fasta extractor
        seq = np.squeeze(self.fasta_extractor([interval]), axis=0)
        # Reformat so that it matches the Basset shape
        seq = np.swapaxes(seq, 1, 0)[:, :, None]
        return {
            "inputs": seq,
            "targets": y,
            "metadata": {
                "ranges": GenomicRanges.from_interval(interval)
            }

self.fasta_extractor = FastaExtractor(self.fasta_file)

        interval = self.bt[idx]

        if self.targets is not None:
            y = self.targets.iloc[idx].values
        else:
            y = {}

        # Run the fasta extractor
        seq = np.squeeze(self.fasta_extractor([interval]), axis=0)
        return {
            "inputs": seq,
            "targets": y,
            "metadata": {
                "ranges": GenomicRanges.from_interval(interval)
            }

dnase = np.squeeze(self.dnase_extractor([interval], axis=0))[:, np.newaxis]
        dnase[np.isnan(dnase)] = 0  # NA fill
        dnase_rc = dnase[::-1]

        bigwig_list = [seq]
        bigwig_rc_list = [seq_rc]
        mappability = np.squeeze(self.mappability_extractor([interval], axis=0))[:, np.newaxis]
        mappability[np.isnan(mappability)] = 0  # NA fill
        mappability_rc = mappability[::-1]
        bigwig_list.append(mappability)
        bigwig_rc_list.append(mappability_rc)
        bigwig_list.append(dnase)
        bigwig_rc_list.append(dnase_rc)

        ranges = GenomicRanges.from_interval(interval)
        ranges_rc = GenomicRanges.from_interval(interval)
        ranges_rc.strand = "-"

        return {
            "inputs": [
                np.concatenate(bigwig_list, axis=-1),  # stack along the last axis
                np.concatenate(bigwig_rc_list, axis=-1),  # RC version
                np.append(self.meta_feat, gencode_counts)
            ],
            "targets": {},  # No Targets
            "metadata": {
                "ranges": ranges,
                "ranges_rc": ranges_rc
            }

# Run the fasta extractor
        seq = np.squeeze(self.fasta_extractor([interval]), axis=0)
        seq_rc = seq[::-1, ::-1]

        # Dnase
        dnase = np.squeeze(self.dnase_extractor([interval], axis=0))[:, np.newaxis]
        dnase[np.isnan(dnase)] = 0  # NA fill
        dnase_rc = dnase[::-1]

        bigwig_list = [seq]
        bigwig_rc_list = [seq_rc]
        bigwig_list.append(dnase)
        bigwig_rc_list.append(dnase_rc)

        ranges = GenomicRanges.from_interval(interval)
        ranges_rc = GenomicRanges.from_interval(interval)
        ranges_rc.strand = "-"

        return {
            "inputs": [
                np.concatenate(bigwig_list, axis=-1),  # stack along the last axis
                np.concatenate(bigwig_rc_list, axis=-1),  # RC version
            ],
            "targets": {},  # No Targets
            "metadata": {
                "ranges": ranges,
                "ranges_rc": ranges_rc
            }

if interval.name is not None:
            y = np.array([float(interval.name)])
        else:
            y = {}

        # Run the fasta extractor
        seq = np.squeeze(self.fasta_extractor([interval]))

        # Reformat so that it matches the Basset shape
        # seq = np.swapaxes(seq, 1, 0)[:,:,None]
        return {
            "inputs": {"data/genome_data_dir": seq},
            "targets": y,
            "metadata": {
                "ranges": GenomicRanges.from_interval(interval)
            }