def METHOD_NAME(
    self,
    batched,
    num_workers,
    checkpoint_interval,
):
    try:
        ingest(
            embedding_function=self.embedding_function,
            embedding_tensor=self.embedding_tensor,
        ).eval(
            batched,
            self.dataset,
            num_workers=num_workers,
            checkpoint_interval=checkpoint_interval,
            verbose=False,
        )
    except Exception as e:
        if isinstance(e.__cause__, IncorrectEmbeddingShapeError):
            raise IncorrectEmbeddingShapeError()
        self.retry_attempt += 1
        last_checkpoint = self.dataset.version_state["commit_node"].parent
        self.total_samples_processed += (
            last_checkpoint.total_samples_processed * self.ingestion_batch_size
        )
        index = int(self.total_samples_processed / self.ingestion_batch_size)
        if isinstance(e, TransformError) and e.index is not None:
            index += e.index
        if self.retry_attempt > MAX_VECTORSTORE_INGESTION_RETRY_ATTEMPTS:
            raise FailedIngestionError(
                f"Ingestion failed at batch index {index}. Maximum retry attempts exceeded. You can resume ingestion "
                "from the latest saved checkpoint.\n"
                "To do that you should run:\n"
                "```\n"
                "deeplake_vector_store.add(\n"
                "    texts=texts,\n"
                "    metadatas=metadatas,\n"
                "    ids=ids,\n"
                "    embeddings=embeddings,\n"
                f"    total_samples_processed={self.total_samples_processed},\n"
                ")\n"
                "```"
            )
        data_ingestion = DataIngestion(
            elements=self.elements,
            dataset=self.dataset,
            embedding_function=self.embedding_function,
            ingestion_batch_size=self.ingestion_batch_size,
            num_workers=num_workers,
            retry_attempt=self.retry_attempt,
            total_samples_processed=self.total_samples_processed,
            logger=self.logger,
            embedding_tensor=self.embedding_tensor,
        )
        data_ingestion.run()