code: def post_predictions(self, document_id: str, model_name: str) -> dict:
          
  
          body = json.dumps({'documentId': document_id, 'modelName': model_name}).encode()
          uri, headers = self._create_signing_headers('POST', '/predictions', body)
  
          post_predictions_response = requests.post(
              url=uri.geturl(),
              headers=headers,
              data=body
          )
  
          response = _json_decode(post_predictions_response)
          return response
docs: Run inference and create a prediction, calls the POST /predictions endpoint.
  
          >>> from las import Client
          >>> client = Client(endpoint='<api endpoint>')
          >>> client.post_predictions(document_id='<document id>', model_name='invoice')
  
          :param document_id: The document id to run inference and create a prediction on
          :type document_id: str
          :param model_name: The name of the model to use for inference
          :type model_name: str
          :return: Prediction on document
          :rtype: dict
          :raises InvalidCredentialsException: If the credentials are invalid
          :raises TooManyRequestsException: If limit of requests per second is reached
          :raises LimitExceededException: If limit of total requests per month is reached
          :raises requests.exception.RequestException: If error was raised by requests