class_docstr: Repacks model artifacts with custom inference entry points.
  
  The SDK automatically adds this step to pipelines that have RegisterModelSteps with models
  that have a custom entry point.
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docstr: The arguments dict that are used to call `create_training_job`.
  
  This first prepares the source bundle for repackinglby placing artifacts
  in locations which the training container will make available to the
  repacking script and then gets the arguments for the training job.
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code: def arguments(self) -> RequestType:
      """The arguments dict that are used to call `create_training_job`.
  
      This first prepares the source bundle for repackinglby placing artifacts
      in locations which the training container will make available to the
      repacking script and then gets the arguments for the training job.
      """
      self._prepare_for_repacking()
      return super(_RepackModelStep, self).arguments
  
imports: from __future__ import absolute_import
  import logging
  import os
  import shutil
  import tarfile
  import tempfile
  from typing import List, Union, Optional, TYPE_CHECKING
  from sagemaker import image_uris
  from sagemaker.inputs import TrainingInput
  from sagemaker.estimator import EstimatorBase
  from sagemaker.sklearn.estimator import SKLearn
  from sagemaker.workflow.entities import RequestType
  from sagemaker.workflow.properties import Properties
  from sagemaker.session import get_create_model_package_request, get_model_package_args
  from sagemaker.workflow.steps import StepTypeEnum, TrainingStep, Step, ConfigurableRetryStep
  from sagemaker.utils import _save_model, download_file_from_url, format_tags
  from sagemaker.workflow.retry import RetryPolicy
  from sagemaker.workflow.utilities import trim_request_dict
label_desc: Use image node_id 5 for calling the _RepackModelStep obj's underlying member method code with example usage: obj.arguments() and returns: super