class_docstr: A supervised learning algorithm used in classification and regression.
  
  Factorization Machines combine the advantages of Support Vector Machines
  with factorization models. It is an extension of a linear model that is
  designed to capture interactions between features within high dimensional
  sparse datasets economically.
docstr: Return a :class:`~sagemaker.amazon.FactorizationMachinesModel`.
  
  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 FactorizationMachinesModel constructor.
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code: def create_model(
      self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs
  ):
      """Return a :class:`~sagemaker.amazon.FactorizationMachinesModel`.
  
      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 FactorizationMachinesModel constructor.
      """
      return FactorizationMachinesModel(
          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.common import RecordSerializer, RecordDeserializer
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.validation import gt, isin, ge
  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 FactorizationMachines obj's underlying member method code with example usage: obj.create_model(vpc_config_override) and returns: FactorizationMachinesModel