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(self, instance_type: str):
      """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.
      """
      self._create_sagemaker_pipeline_model(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 4 for calling the PipelineModel obj's underlying member method code with example usage: obj.create(instance_type) without return types