text: The deployment name of the endpointContent Formatterâ€‹The content_formatter parameter is a handler class for transforming the request and response of an AzureML endpoint to match with required schema. Since there are a wide range of models in the model catalog, each of which may process data differently from one another, a ContentFormatterBase class is provided to allow users to transform data to their liking. Additionally, there are three content formatters already provided:OSSContentFormatter: Formats request and response data for models from the Open Source category in the Model Catalog. Note, that not all models in the Open Source category may follow the same schemaDollyContentFormatter: Formats request and response data for the dolly-v2-12b modelHFContentFormatter: Formats request and response data for text-generation Hugging Face modelsBelow is an example using a summarization model from Hugging Face.Custom Content Formatterâ€‹from typing import Dictfrom langchain.llms.azureml_endpoint import AzureMLOnlineEndpoint, ContentFormatterBaseimport osimport jsonclass CustomFormatter(ContentFormatterBase):    content_type = "application/json"    accepts = "application/json"    def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:        input_str = json.dumps(            {                "inputs": [prompt],                "parameters": model_kwargs,                "options": {"use_cache": False, "wait_for_model": True},            }        )        return str.encode(input_str)    def format_response_payload(self, output: