)
    """
    client: Any  #: :meta private:
    endpoint_name: str = ""
    """The name of the endpoint from the deployed Sagemaker model.
    Must be unique within an AWS Region."""
    region_name: str = ""
    """The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
    credentials_profile_name: Optional[str] = None
    """The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
    has either access keys or role information specified.
    If not specified, the default credential profile or, if on an EC2 instance,
    credentials from IMDS will be used.
    See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
    """
    content_handler: EmbeddingsContentHandler
    """The content handler class that provides an input and
    output transform functions to handle formats between LLM
    and the endpoint.
    """
    """
     Example:
        .. code-block:: python
        from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler
        class ContentHandler(EmbeddingsContentHandler):
                content_type = "application/json"
                accepts = "application/json"
                def transform_input(self, prompts: List[str], model_kwargs: Dict) -> bytes:
                    input_str = json.dumps({prompts: prompts, **model_kwargs})
                    return input_str.encode('utf-8')
                def transform_output(self, output: bytes) -> List[List[float]]:
                    response_json = json.loads(output.read().decode("utf-8"))
                    return response_json["vectors"]
    """  # noqa: E501
    model_kwargs: Optional[Dict] = None
    """Key word arguments to pass to the model."""