class_docstr: A pipeline of SageMaker `Model` instances.
  
  This pipeline can be deployed as an `Endpoint` on SageMaker.
docstr: Delete the SageMaker model backing this pipeline model.
  
  This does not delete the list of SageMaker models used in multiple containers to build
  the inference pipeline.
docstr_tok: ["Delete","the","SageMaker","model","backing","this","pipeline","model",".","This","does","not","delete","the","list","of","SageMaker","models","used","in","multiple","containers","to","build","the","inference","pipeline","."]
code: def delete_model(self):
      """Delete the SageMaker model backing this pipeline model.
  
      This does not delete the list of SageMaker models used in multiple containers to build
      the inference pipeline.
      """
  
      if self.name is None:
          raise ValueError(
              "The SageMaker model must be created before attempting to delete."
          )
  
      self.sagemaker_session.delete_model(self.name)
  
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 8 for calling the PipelineModel obj's underlying member method code with example usage: obj.delete_model() without return types