how to use custom docker images from private repo for training in sagemaker pipelines?

I'm using sagemaker pipeline to create processing and training steps, which works. next, for my training step, i wanted to use my own image , hosted in our own private container registry. I provide subnets, vpc config, but the error message i get is => 'clienterror : Failed to invoke sagemaker: CreateTrainingJob. Error Details: TrainingImageConfig with TrainingRepositoryAccessMode set to VPC must be provided when using a training image from a private docker registry..."

when looking at the sagemaker pipeline documentation , i don't see this configuration in the docs. how can i provide this , when using sagamaker pipeline. ???

pipeline_project
      src
          processing.py
          train.py
      utils
           helper.py

from sagemaker.processing import ScriptProcessor, ProcessingInput, ProcessingOutput

script_processor = ScriptProcessor(command=['python3'],
                image_uri='image_uri',
                role='role_arn',
                instance_count=1,
                instance_type='ml.m5.xlarge', 
)

step_process = ProcessingStep(
        name="ProcessStep",
        processor=script_processor, 
        code = 'src/processing.py'
        input = [ 
                ProcessingInput(
                      input_name="utils"
                      source="src/utils", 
                      destination="/opt/ml/processing/input/src/utils",
                )
)

security_group = ["somegroupid"]
my_subnets = ["subnet-1", subnet-2"]
network_config = NetworkConfig(enable_network_isolation=False, 
                               security_group_ids = security_group , 
                               subnets= my_subnets)

from sagemaker.estimator import Estimator
#training step 
estimate = Estimator (
  entry_point="src/train.py", 
  ....
  network_config= network_config, 
  ....
  image_uri='private_docker_registry/some_image', 
  ...
 )