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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def genes_with_peak(self, peaks, transform_func=None, split=False, intersect_kwargs=None, id_attribute='ID', *args, **kwargs):
 """ Returns a boolean index of genes that have a peak nearby. Parameters peaks : string or pybedtools.BedTool If string, then assume it's a filename to a BED/GFF/GTF file of intervals; otherwise use the pybedtools.BedTool object directly. transform_func : callable This function will be applied to each gene object returned by self.features(). Additional args and kwargs are passed to `transform_func`. For example, if you're looking for peaks within 1kb upstream of TSSs, then pybedtools.featurefuncs.TSS would be a useful `transform_func`, and you could supply additional kwargs of `upstream=1000` and `downstream=0`. This function can return iterables of features, too. For example, you might want to look for peaks falling within the exons of a gene. In this case, `transform_func` should return an iterable of pybedtools.Interval objects. The only requirement is that the `name` field of any feature matches the index of the dataframe. intersect_kwargs : dict kwargs passed to pybedtools.BedTool.intersect. id_attribute : str The attribute in the GTF or GFF file that contains the id of the gene. For meaningful results to be returned, a gene's ID be also found in the index of the dataframe. For GFF files, typically you'd use `id_attribute="ID"`. For GTF files, you'd typically use `id_attribute="gene_id"`. """

        def _transform_func(x):
            """
            In order to support transform funcs that return a single feature or
            an iterable of features, we need to wrap it
            """
            result = transform_func(x)
            if isinstance(result, pybedtools.Interval):
                result = [result]
            for i in result:
                if i:
                    yield result

        intersect_kwargs = intersect_kwargs or {}
        if not self._cached_features:
            self._cached_features = pybedtools\
                .BedTool(self.features())\
                .saveas()

        if transform_func:
            if split:
                features = self._cached_features\
                    .split(_transform_func, *args, **kwargs)
            else:
                features = self._cached_features\
                    .each(transform_func, *args, **kwargs)

        else:
            features = self._cached_features

        hits = list(set([i[id_attribute] for i in features.intersect(
            peaks, **intersect_kwargs)]))
        return self.data.index.isin(hits)