RegisterModelStep throws framework = None error when registering with a Huggingface Estimator

[Adamwgoh]

    model = HuggingFaceModel(
        model_data="s3://finbert-tone/finbert.tar.gz",
        entry_point="./hf_scripts/run_glue.py",
        role=role,
        transformers_version='4.26.0',
        pytorch_version='1.13.1',
        py_version='py39',
    )
    
    hf_est = HuggingFace(
        model_data="s3://finbert-tone/finbert-tone/modeloutput",
        entry_point='run_glue.py',
        source_dir='./hf_scripts',
        checkpoint_s3_uri="s3://finbert-tone/finbert-tone/modeloutput",
        instance_type='ml.p3.2xlarge',
        instance_count=1,
        role=role,
        transformers_version='4.26.0',
        pytorch_version='1.13.1',
          # image_uri=image_uri,
        py_version='py39',
        sagemaker_session=local_sess,
        hyperparameters = hyperparameters
    )
    
    step_register = RegisterModel(
        name="Register-Finbert-Tone-Model",
        estimator=hf_est,
        model=model,
        model_data="s3://my-huggingface-model/model.tar.gz",
        content_types=["text/csv"],
        response_types=["text/csv"],
        inference_instances=["ml.t2.medium", "ml.m5.large"],
        transform_instances=["ml.m5.large"],
        model_package_group_name=model_package_group_name,
        approval_status="PendingManualApproval",
        framework="PYTORCH",
        framework_version="1.13.1",
        entry_point="./hf_scripts/run_glue.py",
        source_dir="./hf_scripts",
    )

    pipeline = Pipeline(
            name=pipeline_name,
            steps=[step_register],
            sagemaker_session=local_sess,
        )
    upsert_response = pipeline.upsert(role_arn=servicerole, description="test")
    print(f"upsert response: {upsert_response}")
    execution = pipeline.start()

Based on the following pipeline steps, I realize that unless I provide a model_uri directly (such as using get_huggingface_llm_image_uri, it will not be parse framework and framework_version over to RegisterModelStep, and cause an error. When we run HuggingfaceModel(() directly, it will query based on framework, pytorch_version etc to get the correct inference container. However, I seem to struggle with doing the same with the following pipeline.

Is there reason why framework is excluded from being parsed when image_uri does not exist, under sagemaker/workflow/_utils.py: _RegisterModelStep?