class_docstr: A pipeline of SageMaker `Model` instances.
  
  This pipeline can be deployed as an `Endpoint` on SageMaker.
docstr: Creates a model package for creating SageMaker models or listing on Marketplace.
  
  Args:
      content_types (list[str] or list[PipelineVariable]): The supported MIME types
          for the input data.
      response_types (list[str] or list[PipelineVariable]): The supported MIME types
          for the output data.
      inference_instances (list[str] or list[PipelineVariable]): A list of the instance
          types that are used to generate inferences in real-time (default: None).
      transform_instances (list[str] or list[PipelineVariable]): A list of the instance types
          on which a transformation job can be run or on which an endpoint can be deployed
          (default: None).
      model_package_name (str or PipelineVariable): Model Package name, exclusive to
          `model_package_group_name`, using `model_package_name` makes the Model Package
          un-versioned (default: None).
      model_package_group_name (str or PipelineVariable): Model Package Group name,
          exclusive to `model_package_name`, using `model_package_group_name` makes
          the Model Package versioned (default: None).
      image_uri (str or PipelineVariable): Inference image uri for the container.
          Model class' self.image will be used if it is None (default: None).
      model_metrics (ModelMetrics): ModelMetrics object (default: None).
      metadata_properties (MetadataProperties): MetadataProperties object (default: None).
      marketplace_cert (bool): A boolean value indicating if the Model Package is certified
          for AWS Marketplace (default: False).
      approval_status (str or PipelineVariable): Model Approval Status, values can
          be "Approved", "Rejected", or "PendingManualApproval"
          (default: "PendingManualApproval").
      description (str): Model Package description (default: None).
      drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
      customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]):
          A dictionary of key-value paired metadata properties (default: None).
      domain (str or PipelineVariable): Domain values can be "COMPUTER_VISION",
          "NATURAL_LANGUAGE_PROCESSING", "MACHINE_LEARNING" (default: None).
      sample_payload_url (str or PipelineVariable): The S3 path where the sample payload
          is stored (default: None).
      task (str or PipelineVariable): Task values which are supported by Inference Recommender
          are "FILL_MASK", "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION",
          "IMAGE_SEGMENTATION", "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
      framework (str or PipelineVariable): Machine learning framework of the model package
          container image (default: None).
      framework_version (str or PipelineVariable): Framework version of the Model Package
          Container Image (default: None).
      nearest_model_name (str or PipelineVariable): Name of a pre-trained machine learning
          benchmarked by Amazon SageMaker Inference Recommender (default: None).
      data_input_configuration (str or PipelineVariable): Input object for the model
          (default: None).
      skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
          validation. Values can be "All" or "None" (default: None).
  
  Returns:
      If ``sagemaker_session`` is a ``PipelineSession`` instance, returns pipeline step
          arguments. Otherwise, returns ``None``
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code: def register(
      self,
      content_types: List[Union[str, PipelineVariable]] = None,
      response_types: List[Union[str, PipelineVariable]] = None,
      inference_instances: Optional[
          List[Union[str, PipelineVariable]]
      ] = None,
      transform_instances: Optional[
          List[Union[str, PipelineVariable]]
      ] = None,
      model_package_name: Optional[Union[str, PipelineVariable]] = None,
      model_package_group_name: Optional[
          Union[str, PipelineVariable]
      ] = None,
      image_uri: Optional[Union[str, PipelineVariable]] = None,
      model_metrics: Optional[ModelMetrics] = None,
      metadata_properties: Optional[MetadataProperties] = None,
      marketplace_cert: bool = False,
      approval_status: Optional[Union[str, PipelineVariable]] = None,
      description: Optional[str] = None,
      drift_check_baselines: Optional[DriftCheckBaselines] = None,
      customer_metadata_properties: Optional[
          Dict[str, Union[str, PipelineVariable]]
      ] = None,
      domain: Optional[Union[str, PipelineVariable]] = None,
      sample_payload_url: Optional[Union[str, PipelineVariable]] = None,
      task: Optional[Union[str, PipelineVariable]] = None,
      framework: Optional[Union[str, PipelineVariable]] = None,
      framework_version: Optional[Union[str, PipelineVariable]] = None,
      nearest_model_name: Optional[Union[str, PipelineVariable]] = None,
      data_input_configuration: Optional[
          Union[str, PipelineVariable]
      ] = None,
      skip_model_validation: Optional[
          Union[str, PipelineVariable]
      ] = None,
  ):
      """Creates a model package for creating SageMaker models or listing on Marketplace.
  
      Args:
          content_types (list[str] or list[PipelineVariable]): The supported MIME types
              for the input data.
          response_types (list[str] or list[PipelineVariable]): The supported MIME types
              for the output data.
          inference_instances (list[str] or list[PipelineVariable]): A list of the instance
              types that are used to generate inferences in real-time (default: None).
          transform_instances (list[str] or list[PipelineVariable]): A list of the instance types
              on which a transformation job can be run or on which an endpoint can be deployed
              (default: None).
          model_package_name (str or PipelineVariable): Model Package name, exclusive to
              `model_package_group_name`, using `model_package_name` makes the Model Package
              un-versioned (default: None).
          model_package_group_name (str or PipelineVariable): Model Package Group name,
              exclusive to `model_package_name`, using `model_package_group_name` makes
              the Model Package versioned (default: None).
          image_uri (str or PipelineVariable): Inference image uri for the container.
              Model class' self.image will be used if it is None (default: None).
          model_metrics (ModelMetrics): ModelMetrics object (default: None).
          metadata_properties (MetadataProperties): MetadataProperties object (default: None).
          marketplace_cert (bool): A boolean value indicating if the Model Package is certified
              for AWS Marketplace (default: False).
          approval_status (str or PipelineVariable): Model Approval Status, values can
              be "Approved", "Rejected", or "PendingManualApproval"
              (default: "PendingManualApproval").
          description (str): Model Package description (default: None).
          drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
          customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]):
              A dictionary of key-value paired metadata properties (default: None).
          domain (str or PipelineVariable): Domain values can be "COMPUTER_VISION",
              "NATURAL_LANGUAGE_PROCESSING", "MACHINE_LEARNING" (default: None).
          sample_payload_url (str or PipelineVariable): The S3 path where the sample payload
              is stored (default: None).
          task (str or PipelineVariable): Task values which are supported by Inference Recommender
              are "FILL_MASK", "IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION",
              "IMAGE_SEGMENTATION", "CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
          framework (str or PipelineVariable): Machine learning framework of the model package
              container image (default: None).
          framework_version (str or PipelineVariable): Framework version of the Model Package
              Container Image (default: None).
          nearest_model_name (str or PipelineVariable): Name of a pre-trained machine learning
              benchmarked by Amazon SageMaker Inference Recommender (default: None).
          data_input_configuration (str or PipelineVariable): Input object for the model
              (default: None).
          skip_model_validation (str or PipelineVariable): Indicates if you want to skip model
              validation. Values can be "All" or "None" (default: None).
  
      Returns:
          If ``sagemaker_session`` is a ``PipelineSession`` instance, returns pipeline step
              arguments. Otherwise, returns ``None``
      """
      for model in self.models:
          if model.model_data is None:
              raise ValueError(
                  "SageMaker Model Package cannot be created without model data."
              )
      if model_package_group_name is not None:
          container_def = self.pipeline_container_def(
              inference_instances[0] if inference_instances else None
          )
          container_def = update_container_with_inference_params(
              framework=framework,
              framework_version=framework_version,
              nearest_model_name=nearest_model_name,
              data_input_configuration=data_input_configuration,
              container_list=container_def,
          )
      else:
          container_def = [
              {
                  "Image": image_uri or model.image_uri,
                  "ModelDataUrl": model.model_data,
              }
              for model in self.models
          ]
  
      model_pkg_args = sagemaker.get_model_package_args(
          content_types,
          response_types,
          inference_instances=inference_instances,
          transform_instances=transform_instances,
          model_package_name=model_package_name,
          model_package_group_name=model_package_group_name,
          model_metrics=model_metrics,
          metadata_properties=metadata_properties,
          marketplace_cert=marketplace_cert,
          approval_status=approval_status,
          description=description,
          container_def_list=container_def,
          drift_check_baselines=drift_check_baselines,
          customer_metadata_properties=customer_metadata_properties,
          domain=domain,
          sample_payload_url=sample_payload_url,
          task=task,
          skip_model_validation=skip_model_validation,
      )
  
      self.sagemaker_session.create_model_package_from_containers(
          **model_pkg_args
      )
  
imports: from __future__ import absolute_import
  from typing import Optional, Dict, List, Union
  import sagemaker
  from sagemaker import ModelMetrics, Model
  from sagemaker.config import ENDPOINT_CONFIG_KMS_KEY_ID_PATH, MODEL_VPC_CONFIG_PATH, MODEL_ENABLE_NETWORK_ISOLATION_PATH, MODEL_EXECUTION_ROLE_ARN_PATH, load_sagemaker_config
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.metadata_properties import MetadataProperties
  from sagemaker.session import Session
  from sagemaker.utils import name_from_image, update_container_with_inference_params, resolve_value_from_config, format_tags
  from sagemaker.transformer import Transformer
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
  from sagemaker.utils import instance_supports_kms
label_desc: Use image node_id 6 for calling the PipelineModel obj's underlying member method code with example usage: obj.register(content_types, response_types, inference_instances, transform_instances, model_package_name, model_package_group_name, image_uri, model_metrics, metadata_properties, marketplace_cert, approval_status, description, drift_check_baselines, customer_metadata_properties, domain, sample_payload_url, task, framework, framework_version, nearest_model_name, data_input_configuration, skip_model_validation) without return types