Carify job fails in spark mode

Thanks for this project. For my project, I'd need to configure some elements of the clarify processing and it would require respective [Docker Files](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-processing-job-configure-container.html) available for modification. More concretely, I am facing timeouts in the endpoint calls due to a very high max batch size/max payload and a slow model, but only when apache spark integration is used, i.e. `instance_count > 1`. In that case, the max payload is for some reason much higher than when spark integration is disabled, leading to longer response times for a batch. Choosing more or a bigger or more powerful instance in the endpoint does not solve the problem.

Can you open-source the Dockerfiles? This would be very beneficial.

In addition, `sagemaker.clarify.SageMakerClarifyProcessor()` should accept an optional `image_uri` argument so I can supply my custom image, but that I can also solve myself by forking the sagemaker sdk and create a PR

Start at the sagemaker.clarify.SageMakerClarifyProcessor() entry point and locate the Docker build definitions for Clarify processing. Confirm how Spark mode selects its image and how endpoint batch limits are configured; done means the Dockerfiles are available for modification and the processor accepts an optional image_uri for a custom image.

Written by the indexing model from the issue text.