How do I Passing categorical feature index to the train step of Sagamaker Pipeline using Catboost from Jumpstart?

My dataset includes both categorical and numerical features, I have tried this: "categorical_index": TrainingInput( s3_data=step_process.properties.ProcessingOutputConfig.Outputs[ "categorical_index" ].S3Output.S3Uri, content_type="text/json", ),

as shown in the code below but it doesn't seem to solve the problem. anybody has an idea?

#****************************************************************************************************************************TRAIN # training step for generating model artifacts from sagemaker import image_uris, model_uris, script_uris from sagemaker import hyperparameters from sagemaker.estimator import Estimator from sagemaker.utils import name_from_base

train_model_id, train_model_version, train_scope = "catboost-classification-model", "*", "training"

# Retrieve the docker image
train_image_uri = image_uris.retrieve(
    region=None,
    framework=None,
    model_id=train_model_id,
    model_version=train_model_version,
    image_scope=train_scope,
    instance_type=training_instance_type
)

# Retrieve the training script
train_source_uri = script_uris.retrieve(
    model_id=train_model_id, model_version=train_model_version, script_scope=train_scope
)

train_model_uri = model_uris.retrieve(
    model_id=train_model_id, model_version=train_model_version, model_scope=train_scope
)


# Retrieve the default hyperparameters for training the model
hyperparameters = hyperparameters.retrieve_default(
    model_id=train_model_id, model_version=train_model_version
)

# [Optional] Override default hyperparameters with custom values
hyperparameters["iterations"] = "1500"
hyperparameters["early_stopping_rounds"] =  "100"
hyperparameters["eval_metric"] =  "F1" 
hyperparameters["learning_rate"] =  "0.2"
print(hyperparameters)    



s3_output_location= f"s3://{default_bucket}/{base_job_prefix}/"

# Create SageMaker Estimator instance
tabular_estimator = Estimator(
    role=role,
    image_uri=train_image_uri,
    source_dir=train_source_uri,
    model_uri=train_model_uri,
    entry_point="transfer_learning.py",
    instance_count=1,
    instance_type=training_instance_type,
    max_run=360000,
    hyperparameters=hyperparameters,
    output_path=s3_output_location
)


# Launch a SageMaker Training job by passing s3 path of the training data
from sagemaker.workflow.steps import CacheConfig
cache_config = CacheConfig(enable_caching=True, expire_after="PT1H")    

step_train = TrainingStep(
    name="TrainTeesModel",
    estimator=tabular_estimator,
    inputs={
        "training": TrainingInput(
            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
                "train"
            ].S3Output.S3Uri,
            content_type="text/csv",
        ),
        "validation": TrainingInput(
            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
                "validation"
            ].S3Output.S3Uri,
            content_type="text/csv",
        ),
        "categorical_index": TrainingInput(
            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
                "categorical_index"
            ].S3Output.S3Uri,
            content_type="text/json",
        ),
    },        
)