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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 _collate_metadata(self):
 """Turns a list of objects associated with a classification result into a DataFrame of metadata. Returns ------- None, but stores a result in self._cached. """

        import pandas as pd

        DEFAULT_FIELDS = None
        metadata = []

        for c in self._classifications:
            m = c.sample.metadata

            if DEFAULT_FIELDS is None:
                DEFAULT_FIELDS = list(m._resource._schema["properties"].keys())
                DEFAULT_FIELDS.remove("$uri")
                DEFAULT_FIELDS.remove("sample")

            metadatum = {f: getattr(m, f) for f in DEFAULT_FIELDS}
            metadatum["classification_id"] = c.id
            metadatum["sample_id"] = m.sample.id
            metadatum["metadata_id"] = m.id
            metadatum["created_at"] = m.sample.created_at
            metadatum["filename"] = c.sample.filename

            metadatum.update(m.custom)
            metadata.append(metadatum)

        if metadata:
            metadata = pd.DataFrame(metadata).set_index("classification_id")
        else:
            metadata = pd.DataFrame(
                columns=["classification_id", "sample_id", "metadata_id", "created_at"]
            )

        self._cached["metadata"] = metadata