class_docstr: An Scikit-learn SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``.
docstr: Container definition with framework configuration set in model environment variables.
  
  Args:
      instance_type (str): The EC2 instance type to deploy this Model to.
          This parameter is unused because Scikit-learn supports only CPU.
      accelerator_type (str): The Elastic Inference accelerator type to
          deploy to the instance for loading and making inferences to the
          model. This parameter is unused because accelerator types
          are not supported by SKLearnModel.
      serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
          Specifies configuration related to serverless endpoint. Instance type is
          not provided in serverless inference. So this is used to find image URIs.
      accept_eula (bool): For models that require a Model Access Config, specify True or
          False to indicate whether model terms of use have been accepted.
          The `accept_eula` value must be explicitly defined as `True` in order to
          accept the end-user license agreement (EULA) that some
          models require. (Default: None).
  
  Returns:
      dict[str, str]: A container definition object usable with the
      CreateModel API.
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code: def prepare_container_def(
      self,
      instance_type=None,
      accelerator_type=None,
      serverless_inference_config=None,
      accept_eula=None,
  ):
      """Container definition with framework configuration set in model environment variables.
  
      Args:
          instance_type (str): The EC2 instance type to deploy this Model to.
              This parameter is unused because Scikit-learn supports only CPU.
          accelerator_type (str): The Elastic Inference accelerator type to
              deploy to the instance for loading and making inferences to the
              model. This parameter is unused because accelerator types
              are not supported by SKLearnModel.
          serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
              Specifies configuration related to serverless endpoint. Instance type is
              not provided in serverless inference. So this is used to find image URIs.
          accept_eula (bool): For models that require a Model Access Config, specify True or
              False to indicate whether model terms of use have been accepted.
              The `accept_eula` value must be explicitly defined as `True` in order to
              accept the end-user license agreement (EULA) that some
              models require. (Default: None).
  
      Returns:
          dict[str, str]: A container definition object usable with the
          CreateModel API.
      """
      if accelerator_type:
          raise ValueError(
              "Accelerator types are not supported for Scikit-Learn."
          )
  
      deploy_image = self.image_uri
      if not deploy_image:
          deploy_image = self.serving_image_uri(
              self.sagemaker_session.boto_region_name, instance_type
          )
  
      deploy_key_prefix = model_code_key_prefix(
          self.key_prefix, self.name, deploy_image
      )
      self._upload_code(
          key_prefix=deploy_key_prefix,
          repack=self.enable_network_isolation(),
      )
      deploy_env = dict(self.env)
      deploy_env.update(self._script_mode_env_vars())
  
      if self.model_server_workers:
          deploy_env[
              MODEL_SERVER_WORKERS_PARAM_NAME.upper()
          ] = to_string(self.model_server_workers)
      model_data_uri = (
          self.repacked_model_data
          if self.enable_network_isolation()
          else self.model_data
      )
      return sagemaker.container_def(
          deploy_image,
          model_data_uri,
          deploy_env,
          accept_eula=accept_eula,
      )
  
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional, List, Dict
  import sagemaker
  from sagemaker import image_uris, ModelMetrics
  from sagemaker.deserializers import NumpyDeserializer
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.fw_utils import model_code_key_prefix, validate_version_or_image_args
  from sagemaker.metadata_properties import MetadataProperties
  from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
  from sagemaker.predictor import Predictor
  from sagemaker.serializers import NumpySerializer
  from sagemaker.sklearn import defaults
  from sagemaker.utils import to_string
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 3 for calling the SKLearnModel obj's underlying member method code with example usage: obj.prepare_container_def(instance_type, accelerator_type, serverless_inference_config, accept_eula) and returns: sagemaker