class_docstr: Handle end-to-end training and deployment of XGBoost booster training.
  
  It can also handle training using customer provided XGBoost entry point script.
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docstr: Create a SageMaker ``XGBoostModel`` object that can be deployed to an ``Endpoint``.
  
  Args:
      role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also used
          during transform jobs. If not specified, the role from the Estimator will be used.
      model_server_workers (int): Optional. The number of worker processes used by the
          inference server. If None, server will use one worker per vCPU.
      vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the
          model.
          Default: use subnets and security groups from this Estimator.
          * 'Subnets' (list[str]): List of subnet ids.
          * 'SecurityGroupIds' (list[str]): List of security group ids.
      entry_point (str): Path (absolute or relative) to the local Python source file which
          should be executed as the entry point to training. If ``source_dir`` is specified,
          then ``entry_point`` must point to a file located at the root of ``source_dir``.
          If not specified, the training entry point is used.
      source_dir (str): Path (absolute or relative) to a directory with any other serving
          source code dependencies aside from the entry point file.
          If not specified, the model source directory from training is used.
      dependencies (list[str]): A list of paths to directories (absolute or relative) with
          any additional libraries that will be exported to the container.
          If not specified, the dependencies from training are used.
          This is not supported with "local code" in Local Mode.
      **kwargs: Additional kwargs passed to the :class:`~sagemaker.xgboost.model.XGBoostModel`
          constructor.
  
  Returns:
      sagemaker.xgboost.model.XGBoostModel: A SageMaker ``XGBoostModel`` object.
          See :func:`~sagemaker.xgboost.model.XGBoostModel` for full details.
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code: def create_model(
      self,
      model_server_workers=None,
      role=None,
      vpc_config_override=VPC_CONFIG_DEFAULT,
      entry_point=None,
      source_dir=None,
      dependencies=None,
      **kwargs,
  ):
      """Create a SageMaker ``XGBoostModel`` object that can be deployed to an ``Endpoint``.
  
      Args:
          role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also used
              during transform jobs. If not specified, the role from the Estimator will be used.
          model_server_workers (int): Optional. The number of worker processes used by the
              inference server. If None, server will use one worker per vCPU.
          vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the
              model.
              Default: use subnets and security groups from this Estimator.
              * 'Subnets' (list[str]): List of subnet ids.
              * 'SecurityGroupIds' (list[str]): List of security group ids.
          entry_point (str): Path (absolute or relative) to the local Python source file which
              should be executed as the entry point to training. If ``source_dir`` is specified,
              then ``entry_point`` must point to a file located at the root of ``source_dir``.
              If not specified, the training entry point is used.
          source_dir (str): Path (absolute or relative) to a directory with any other serving
              source code dependencies aside from the entry point file.
              If not specified, the model source directory from training is used.
          dependencies (list[str]): A list of paths to directories (absolute or relative) with
              any additional libraries that will be exported to the container.
              If not specified, the dependencies from training are used.
              This is not supported with "local code" in Local Mode.
          **kwargs: Additional kwargs passed to the :class:`~sagemaker.xgboost.model.XGBoostModel`
              constructor.
  
      Returns:
          sagemaker.xgboost.model.XGBoostModel: A SageMaker ``XGBoostModel`` object.
              See :func:`~sagemaker.xgboost.model.XGBoostModel` for full details.
      """
      role = role or self.role
      kwargs["name"] = self._get_or_create_name(kwargs.get("name"))
  
      if "image_uri" not in kwargs:
          kwargs["image_uri"] = self.image_uri
  
      return XGBoostModel(
          self.model_data,
          role,
          entry_point or self._model_entry_point(),
          framework_version=self.framework_version,
          source_dir=(source_dir or self._model_source_dir()),
          container_log_level=self.container_log_level,
          code_location=self.code_location,
          py_version=self.py_version,
          model_server_workers=model_server_workers,
          sagemaker_session=self.sagemaker_session,
          vpc_config=self.get_vpc_config(vpc_config_override),
          dependencies=(dependencies or self.dependencies),
          **kwargs,
      )
  
file_path: sagemaker-python-sdk/src/sagemaker/xgboost/estimator.py
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional, Dict
  from sagemaker import image_uris
  from sagemaker.deprecations import renamed_kwargs
  from sagemaker.estimator import Framework, _TrainingJob
  from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, UploadedCode
  from sagemaker.session import Session
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.xgboost import defaults
  from sagemaker.xgboost.model import XGBoostModel
  from sagemaker.xgboost.utils import validate_py_version, validate_framework_version
label_desc: Use image node_id 2 for calling the XGBoost obj's underlying member method code with example usage: obj.create_model(model_server_workers, role, vpc_config_override, entry_point, source_dir, dependencies) and returns: XGBoostModel