class_docstr: Handle end-to-end training and deployment of user-provided TensorFlow code.
class_docstr_tok: ["Handle","end-to-end","training","and","deployment","of","user-provided","TensorFlow","code","."]
docstr: Creates ``TensorFlowModel`` object to be used for creating SageMaker model entities.
  
  This can be done by deploying it to a SageMaker endpoint,
  or starting SageMaker Batch Transform jobs.
  
  Args:
      role (str): The ``TensorFlowModel``, which is also used during transform jobs.
          If not specified, the role from the Estimator is used.
      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 and ``endpoint_type`` is 'tensorflow-serving',
          no entry point is used. If ``endpoint_type`` is also ``None``,
          then the training entry point is used.
      source_dir (str): Path (absolute or relative or an S3 URI) to a directory with any other
          serving source code dependencies aside from the entry point file (default: None).
      dependencies (list[str]): A list of paths to directories (absolute or relative) with
          any additional libraries that will be exported to the container (default: None).
      **kwargs: Additional kwargs passed to
          :class:`~sagemaker.tensorflow.model.TensorFlowModel`.
  
  Returns:
      sagemaker.tensorflow.model.TensorFlowModel: A ``TensorFlowModel`` object.
          See :class:`~sagemaker.tensorflow.model.TensorFlowModel` for full details.
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code: def create_model(
      self,
      role=None,
      vpc_config_override=VPC_CONFIG_DEFAULT,
      entry_point=None,
      source_dir=None,
      dependencies=None,
      **kwargs,
  ):
      """Creates ``TensorFlowModel`` object to be used for creating SageMaker model entities.
  
      This can be done by deploying it to a SageMaker endpoint,
      or starting SageMaker Batch Transform jobs.
  
      Args:
          role (str): The ``TensorFlowModel``, which is also used during transform jobs.
              If not specified, the role from the Estimator is used.
          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 and ``endpoint_type`` is 'tensorflow-serving',
              no entry point is used. If ``endpoint_type`` is also ``None``,
              then the training entry point is used.
          source_dir (str): Path (absolute or relative or an S3 URI) to a directory with any other
              serving source code dependencies aside from the entry point file (default: None).
          dependencies (list[str]): A list of paths to directories (absolute or relative) with
              any additional libraries that will be exported to the container (default: None).
          **kwargs: Additional kwargs passed to
              :class:`~sagemaker.tensorflow.model.TensorFlowModel`.
  
      Returns:
          sagemaker.tensorflow.model.TensorFlowModel: A ``TensorFlowModel`` object.
              See :class:`~sagemaker.tensorflow.model.TensorFlowModel` for full details.
      """
      kwargs["name"] = self._get_or_create_name(kwargs.get("name"))
  
      if "image_uri" not in kwargs:
          kwargs["image_uri"] = self.image_uri
  
      if "enable_network_isolation" not in kwargs:
          kwargs[
              "enable_network_isolation"
          ] = self.enable_network_isolation()
  
      return TensorFlowModel(
          model_data=self.model_data,
          role=role or self.role,
          container_log_level=self.container_log_level,
          framework_version=self.framework_version,
          sagemaker_session=self.sagemaker_session,
          vpc_config=self.get_vpc_config(vpc_config_override),
          entry_point=entry_point,
          source_dir=source_dir,
          dependencies=dependencies,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  import logging
  from typing import Optional, Union, Dict
  from packaging import version
  from sagemaker import image_uris, s3, utils
  from sagemaker.deprecations import renamed_kwargs
  from sagemaker.estimator import Framework, EstimatorBase
  import sagemaker.fw_utils
  from sagemaker.tensorflow import defaults
  from sagemaker.tensorflow.model import TensorFlowModel
  from sagemaker.transformer import Transformer
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.tensorflow.training_compiler.config import TrainingCompilerConfig
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.utils import format_tags
label_desc: Use image node_id 6 for calling the TensorFlow obj's underlying member method code with example usage: obj.create_model(role, vpc_config_override, entry_point, source_dir, dependencies) and returns: TensorFlowModel