class_docstr: A general-purpose neural embedding algorithm that is highly customizable.
  
  It can learn low-dimensional dense embeddings of high-dimensional objects. The embeddings
  are learned in a way that preserves the semantics of the relationship between pairs of
  objects in the original space in the embedding space.
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docstr: Return a :class:`~sagemaker.amazon.Object2VecModel`.
  
  It references the latest s3 model data produced by this Estimator.
  
  Args:
      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.
      **kwargs: Additional kwargs passed to the Object2VecModel constructor.
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code: def create_model(
      self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs
  ):
      """Return a :class:`~sagemaker.amazon.Object2VecModel`.
  
      It references the latest s3 model data produced by this Estimator.
  
      Args:
          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.
          **kwargs: Additional kwargs passed to the Object2VecModel constructor.
      """
      return Object2VecModel(
          self.model_data,
          self.role,
          sagemaker_session=self.sagemaker_session,
          vpc_config=self.get_vpc_config(vpc_config_override),
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  from typing import Union, Optional
  from sagemaker import image_uris
  from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.validation import ge, le, isin
  from sagemaker.predictor import Predictor
  from sagemaker.model import Model
  from sagemaker.session import Session
  from sagemaker.utils import pop_out_unused_kwarg
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 2 for calling the Object2Vec obj's underlying member method code with example usage: obj.create_model(vpc_config_override) and returns: Object2VecModel