class_docstr: JumpStartModel class.
  
  This class sets defaults based on the model ID and version.
class_docstr_tok: ["JumpStartModel","class",".","This","class","sets","defaults","based","on","the","model","ID","and","version","."]
docstr: Create a SageMaker Model Entity
  
  Args:
      instance_type (str): Optional. 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. (Default: None).
      accelerator_type (str): Optional. Type of Elastic Inference accelerator to
          attach to an endpoint for model loading and inference, for
          example, 'ml.eia1.medium'. If not specified, no Elastic
          Inference accelerator will be attached to the endpoint. (Default: None).
      tags (Optional[Tags]): Optional. The list of tags to add to
          the model. Example: >>> tags = [{'Key': 'tagname', 'Value':
          'tagvalue'}] For more information about tags, see
          https://boto3.amazonaws.com/v1/documentation
          /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
          (Default: None).
      serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
          Optional. Specifies configuration related to serverless endpoint. Instance type is
          not provided in serverless inference. So this is used to find image URIs.
          (Default: None).
      kwargs: Keyword arguments coming from the caller. This class does not require
          any so they are ignored.
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code: def _create_sagemaker_model(
      self,
      instance_type=None,
      accelerator_type=None,
      tags=None,
      serverless_inference_config=None,
      **kwargs,
  ):
      """Create a SageMaker Model Entity
  
      Args:
          instance_type (str): Optional. 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. (Default: None).
          accelerator_type (str): Optional. Type of Elastic Inference accelerator to
              attach to an endpoint for model loading and inference, for
              example, 'ml.eia1.medium'. If not specified, no Elastic
              Inference accelerator will be attached to the endpoint. (Default: None).
          tags (Optional[Tags]): Optional. The list of tags to add to
              the model. Example: >>> tags = [{'Key': 'tagname', 'Value':
              'tagvalue'}] For more information about tags, see
              https://boto3.amazonaws.com/v1/documentation
              /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
              (Default: None).
          serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
              Optional. Specifies configuration related to serverless endpoint. Instance type is
              not provided in serverless inference. So this is used to find image URIs.
              (Default: None).
          kwargs: Keyword arguments coming from the caller. This class does not require
              any so they are ignored.
      """
  
      tags = format_tags(tags)
  
      # if the user inputs a model artifact uri, do not use model package arn to create
      # inference endpoint.
      if self.model_package_arn and not self._model_data_is_set:
          # When a ModelPackageArn is provided we just create the Model
          model_package = ModelPackage(
              role=self.role,
              model_data=self.model_data,
              model_package_arn=self.model_package_arn,
              sagemaker_session=self.sagemaker_session,
              predictor_cls=self.predictor_cls,
              vpc_config=self.vpc_config,
          )
          if self.name is not None:
              model_package.name = self.name
          if self.env is not None:
              model_package.env = self.env
          model_package._create_sagemaker_model(
              instance_type=instance_type,
              accelerator_type=accelerator_type,
              tags=tags,
              serverless_inference_config=serverless_inference_config,
              **kwargs,
          )
          if (
              self._base_name is None
              and model_package._base_name is not None
          ):
              self._base_name = model_package._base_name
          if self.name is None and model_package.name is not None:
              self.name = model_package.name
      else:
          super(JumpStartModel, self)._create_sagemaker_model(
              instance_type=instance_type,
              accelerator_type=accelerator_type,
              tags=tags,
              serverless_inference_config=serverless_inference_config,
              **kwargs,
          )
  
imports: from __future__ import absolute_import
  from typing import Dict, List, Optional, Union
  from sagemaker import payloads
  from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig
  from sagemaker.base_deserializers import BaseDeserializer
  from sagemaker.base_serializers import BaseSerializer
  from sagemaker.explainer.explainer_config import ExplainerConfig
  from sagemaker.jumpstart.accessors import JumpStartModelsAccessor
  from sagemaker.jumpstart.enums import JumpStartScriptScope
  from sagemaker.jumpstart.exceptions import INVALID_MODEL_ID_ERROR_MSG
  from sagemaker.jumpstart.factory.model import get_default_predictor, get_deploy_kwargs, get_init_kwargs, get_register_kwargs
  from sagemaker.jumpstart.types import JumpStartSerializablePayload
  from sagemaker.jumpstart.utils import is_valid_model_id
  from sagemaker.utils import stringify_object, format_tags, Tags
  from sagemaker.model import Model, ModelPackage
  from sagemaker.model_monitor.data_capture_config import DataCaptureConfig
  from sagemaker.predictor import PredictorBase
  from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig
  from sagemaker.session import Session
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.model_metrics import ModelMetrics
  from sagemaker.metadata_properties import MetadataProperties
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
  from sagemaker.enums import EndpointType
label_desc: Use image node_id 4 for calling the JumpStartModel obj's underlying member method code with example usage: obj._create_sagemaker_model(instance_type, accelerator_type, tags, serverless_inference_config) without return types