def _post(self, data: List[str]) -> List[List[float]]:
        """
        Sends a POST request to the DeepInfra Inference API with the given data and returns the API response.
        Input data is chunked into batches to avoid exceeding the maximum batch size (1024).

        Args:
            data (List[str]): A list of strings to be embedded.

        Returns:
            dict: A dictionary containing embeddings from the API.

        """
        url = self.get_url()
        chunked_data = _chunk(data, self.embed_batch_size)
        embeddings = []
        for chunk in chunked_data:
            response = requests.post(
                url,
                json={
                    "inputs": chunk,
                },
                headers=self._get_headers(),
            )
            response.raise_for_status()
            embeddings.extend(response.json()["embeddings"])
        return embeddings

        Sends a POST request to the DeepInfra Inference API with the given data and returns the API response.
        Input data is chunked into batches to avoid exceeding the maximum batch size (1024).

        Args:
            data (List[str]): A list of strings to be embedded.

        Returns:
            dict: A dictionary containing embeddings from the API.

        
llama-index-integrations/embeddings/llama-index-embeddings-deepinfra/llama_index/embeddings/deepinfra/base.py