Redshift ML with multi-container Sagemaker model

Is it possible to use Redshift ML with a Sagemaker multi-container model?

The closest I can find is the documentation that mentions the syntax for BYOM remote inference (https://docs.aws.amazon.com/redshift/latest/dg/r_create_model_use_cases.html):

CREATE MODEL model_name 
    FUNCTION function_name ( data_type [, ...] ) 
    RETURNS data_type
    SAGEMAKER 'endpoint_name'[:'model_name']
    IAM_ROLE { default };

Which specifies a model_name parameter. However, I'm dealing with a single Sagemaker model with multiple containers, running in Direct mode.

For reference, referencing a specific container is possible using boto3 (I'm dapting the example from here: https://docs.aws.amazon.com/sagemaker/latest/dg/neo-requests-boto3.html):

import boto3

import json
 
endpoint = 'insert name of your endpoint here'
 
runtime = boto3.Session().client('sagemaker-runtime')
 
# Read image into memory
with open(image, 'rb') as f:
    payload = f.read()
# Send image via InvokeEndpoint API
response = runtime.invoke_endpoint(
    EndpointName=endpoint,
    ContentType='application/x-image',
    Body=payload,
    TargetContainerHostname='my-container' # Here I would specify each individual container.
)

# Unpack response
result = json.loads(response['Body'].read().decode())