class_docstr: A pipeline of SageMaker `Model` instances.
  
  This pipeline can be deployed as an `Endpoint` on SageMaker.
docstr: The pipeline definition for deploying this model.
  
  This is the dict created by ``sagemaker.pipeline_container_def()``.
  
  The instance type to be used may be specified.
  
  Subclasses can override this to provide custom container definitions
  for deployment to a specific instance type. Called by ``deploy()``.
  
  Args:
      instance_type (str): The EC2 instance type to deploy this Model to.
          For example, 'ml.p2.xlarge'.
  
  Returns:
      list[dict[str, str]]: A list of container definition objects usable
      with the CreateModel API in the scenario of multiple containers
      (Inference Pipeline).
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code: def pipeline_container_def(self, instance_type=None):
      """The pipeline definition for deploying this model.
  
      This is the dict created by ``sagemaker.pipeline_container_def()``.
  
      The instance type to be used may be specified.
  
      Subclasses can override this to provide custom container definitions
      for deployment to a specific instance type. Called by ``deploy()``.
  
      Args:
          instance_type (str): The EC2 instance type to deploy this Model to.
              For example, 'ml.p2.xlarge'.
  
      Returns:
          list[dict[str, str]]: A list of container definition objects usable
          with the CreateModel API in the scenario of multiple containers
          (Inference Pipeline).
      """
  
      return sagemaker.pipeline_container_def(
          self.models, instance_type
      )
  
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 2 for calling the PipelineModel obj's underlying member method code with example usage: obj.pipeline_container_def(instance_type) and returns: sagemaker