How to use the sagemaker.fw_utils.UploadedCode function in sagemaker

def test_tar_and_upload_dir_with_subdirectory(sagemaker_session, tmpdir):
    file_tree(tmpdir, ["src-dir/sub/train.py"])
    source_dir = os.path.join(str(tmpdir), "src-dir")

    with patch("shutil.rmtree"):
        result = fw_utils.tar_and_upload_dir(
            sagemaker_session, "bucket", "prefix", "train.py", source_dir
        )

    assert result == fw_utils.UploadedCode(
        s3_prefix="s3://bucket/prefix/sourcedir.tar.gz", script_name="train.py"
    )

    assert {"/sub/train.py"} == list_source_dir_files(sagemaker_session, tmpdir)

def test_tar_and_upload_dir_no_directory_bare_filename(sagemaker_session, tmpdir):
    source_dir = file_tree(tmpdir, ["train.py"])
    entrypoint = "train.py"

    with patch("shutil.rmtree"):
        with cd(source_dir):
            result = fw_utils.tar_and_upload_dir(
                sagemaker_session, "bucket", "prefix", entrypoint, None
            )

    assert result == fw_utils.UploadedCode(
        s3_prefix="s3://bucket/prefix/sourcedir.tar.gz", script_name="train.py"
    )

    assert {"/train.py"} == list_source_dir_files(sagemaker_session, tmpdir)

if repack:
            bucket = self.bucket or self.sagemaker_session.default_bucket()
            repacked_model_data = "s3://" + os.path.join(bucket, key_prefix, "model.tar.gz")

            utils.repack_model(
                inference_script=self.entry_point,
                source_directory=self.source_dir,
                dependencies=self.dependencies,
                model_uri=self.model_data,
                repacked_model_uri=repacked_model_data,
                sagemaker_session=self.sagemaker_session,
                kms_key=self.model_kms_key,
            )

            self.repacked_model_data = repacked_model_data
            self.uploaded_code = UploadedCode(
                s3_prefix=self.repacked_model_data, script_name=os.path.basename(self.entry_point)
            )

job_name (str): The new job name included in the submit S3 URI

    Returns:
        str: The updated S3 URI of framework source directory
    """
    if estimator.uploaded_code is None:
        return

    pattern = r"(?<=/)[^/]+?(?=/source/sourcedir.tar.gz)"

    # update the S3 URI with the latest training job.
    # s3://path/old_job/source/sourcedir.tar.gz will become s3://path/new_job/source/sourcedir.tar.gz
    submit_uri = estimator.uploaded_code.s3_prefix
    submit_uri = re.sub(pattern, job_name, submit_uri)
    script_name = estimator.uploaded_code.script_name
    estimator.uploaded_code = fw_utils.UploadedCode(submit_uri, script_name)

model.py_version,
        )

    base_name = utils.base_name_from_image(deploy_image)
    model.name = model.name or utils.name_from_base(base_name)

    bucket = model.bucket or model.sagemaker_session._default_bucket
    script = os.path.basename(model.entry_point)
    key = "{}/source/sourcedir.tar.gz".format(model.name)

    if model.source_dir and model.source_dir.lower().startswith("s3://"):
        code_dir = model.source_dir
        model.uploaded_code = fw_utils.UploadedCode(s3_prefix=code_dir, script_name=script)
    else:
        code_dir = "s3://{}/{}".format(bucket, key)
        model.uploaded_code = fw_utils.UploadedCode(s3_prefix=code_dir, script_name=script)
        s3_operations["S3Upload"] = [
            {"Path": model.source_dir or script, "Bucket": bucket, "Key": key, "Tar": True}
        ]

    deploy_env = dict(model.env)
    deploy_env.update(model._framework_env_vars())

    try:
        if model.model_server_workers:
            deploy_env[sagemaker.model.MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = str(
                model.model_server_workers
            )
    except AttributeError:
        # This applies to a FrameworkModel which is not SageMaker Deep Learning Framework Model
        pass

bucket, key = fw_utils.parse_s3_url(estimator.code_location)
        key = os.path.join(key, estimator._current_job_name, "source", "sourcedir.tar.gz")
    elif estimator.uploaded_code is not None:
        bucket, key = fw_utils.parse_s3_url(estimator.uploaded_code.s3_prefix)
    else:
        bucket = estimator.sagemaker_session._default_bucket
        key = os.path.join(estimator._current_job_name, "source", "sourcedir.tar.gz")

    script = os.path.basename(estimator.entry_point)

    if estimator.source_dir and estimator.source_dir.lower().startswith("s3://"):
        code_dir = estimator.source_dir
        estimator.uploaded_code = fw_utils.UploadedCode(s3_prefix=code_dir, script_name=script)
    else:
        code_dir = "s3://{}/{}".format(bucket, key)
        estimator.uploaded_code = fw_utils.UploadedCode(s3_prefix=code_dir, script_name=script)
        s3_operations["S3Upload"] = [
            {
                "Path": estimator.source_dir or estimator.entry_point,
                "Bucket": bucket,
                "Key": key,
                "Tar": True,
            }
        ]
    estimator._hyperparameters[sagemaker.model.DIR_PARAM_NAME] = code_dir
    estimator._hyperparameters[sagemaker.model.SCRIPT_PARAM_NAME] = script
    estimator._hyperparameters[
        sagemaker.model.CLOUDWATCH_METRICS_PARAM_NAME
    ] = estimator.enable_cloudwatch_metrics
    estimator._hyperparameters[
        sagemaker.model.CONTAINER_LOG_LEVEL_PARAM_NAME
    ] = estimator.container_log_level