class_docstr: A pipeline of SageMaker `Model` instances.
  
  This pipeline can be deployed as an `Endpoint` on SageMaker.
docstr: Create a SageMaker Model Entity
  
  Args:
      instance_type (str): The EC2 instance type that this Model will be
          used for, this is only used to determine if the image needs GPU
          support or not.
docstr_tok: ["Create","a","SageMaker","Model","Entity","Args",":","instance_type","(","str",")",":","The","EC2","instance","type","that","this","Model","will","be","used","for",",","this","is","only","used","to","determine","if","the","image","needs","GPU","support","or","not","."]
code: def _create_sagemaker_pipeline_model(self, instance_type):
      """Create a SageMaker Model Entity
  
      Args:
          instance_type (str): The EC2 instance type that this Model will be
              used for, this is only used to determine if the image needs GPU
              support or not.
      """
      if not self.sagemaker_session:
          self.sagemaker_session = Session(
              sagemaker_config=self._sagemaker_config
          )
  
      containers = self.pipeline_container_def(instance_type)
  
      self.name = self.name or name_from_image(containers[0]["Image"])
      create_model_args = dict(
          name=self.name,
          role=self.role,
          container_defs=containers,
          vpc_config=self.vpc_config,
          enable_network_isolation=self.enable_network_isolation,
      )
      self.sagemaker_session.create_model(**create_model_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 5 for calling the PipelineModel obj's underlying member method code with example usage: obj._create_sagemaker_pipeline_model(instance_type) without return types