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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 to_otu(self, biom_id=None):
 """Converts a list of objects associated with a classification result into a `dict` resembling an OTU table. Parameters biom_id : `string`, optional Optionally specify an `id` field for the generated v1 BIOM file. Returns ------- otu_table : `OrderedDict` A BIOM OTU table, returned as a Python OrderedDict (can be dumped to JSON) """

        otu_format = "Biological Observation Matrix 1.0.0"

        # Note: This is exact format URL is required by https://github.com/biocore/biom-format
        otu_url = "http://biom-format.org"

        otu = OrderedDict(
            {
                "id": biom_id,
                "format": otu_format,
                "format_url": otu_url,
                "type": "OTU table",
                "generated_by": "One Codex API V1",
                "date": datetime.now().isoformat(),
                "rows": [],
                "columns": [],
                "matrix_type": "sparse",
                "matrix_element_type": "int",
            }
        )

        rows = defaultdict(dict)

        tax_ids_to_names = {}
        for classification in self._classifications:
            col_id = len(otu["columns"])  # 0 index

            # Re-encoding the JSON is a bit of a hack, but
            # we need a ._to_dict() method that properly
            # resolves references and don't have one at the moment
            columns_entry = {
                "id": str(classification.id),
                "sample_id": str(classification.sample.id),
                "sample_filename": classification.sample.filename,
                "metadata": json.loads(
                    classification.sample.metadata._to_json(include_references=False)
                ),
            }

            otu["columns"].append(columns_entry)
            sample_df = classification.table()

            for row in sample_df.iterrows():
                tax_id = row[1]["tax_id"]
                tax_ids_to_names[tax_id] = row[1]["name"]
                rows[tax_id][col_id] = int(row[1]["readcount"])

        num_rows = len(rows)
        num_cols = len(otu["columns"])

        otu["shape"] = [num_rows, num_cols]
        otu["data"] = []

        for present_taxa in sorted(rows):
            # add the row entry
            row_id = len(otu["rows"])
            otu["rows"].append(
                {"id": present_taxa, "metadata": {"taxonomy": tax_ids_to_names[present_taxa]}}
            )

            for sample_with_hit in rows[present_taxa]:
                counts = rows[present_taxa][sample_with_hit]
                otu["data"].append([row_id, sample_with_hit, counts])

        return otu