class_docstr: A ``FrameworkModel`` implementation for inference with TensorFlow Serving.
docstr: Deploy a Tensorflow ``Model`` to a SageMaker ``Endpoint``.
docstr_tok: ["Deploy","a","Tensorflow","``","Model","``","to","a","SageMaker","``","Endpoint","``","."]
code: def deploy(
      self,
      initial_instance_count=None,
      instance_type=None,
      serializer=None,
      deserializer=None,
      accelerator_type=None,
      endpoint_name=None,
      tags=None,
      kms_key=None,
      wait=True,
      data_capture_config=None,
      async_inference_config=None,
      serverless_inference_config=None,
      volume_size=None,
      model_data_download_timeout=None,
      container_startup_health_check_timeout=None,
      inference_recommendation_id=None,
      explainer_config=None,
      **kwargs,
  ):
      """Deploy a Tensorflow ``Model`` to a SageMaker ``Endpoint``."""
      if accelerator_type and not self._eia_supported():
          msg = (
              "The TensorFlow version %s doesn't support EIA."
              % self.framework_version
          )
          raise AttributeError(msg)
  
      return super(TensorFlowModel, self).deploy(
          initial_instance_count=initial_instance_count,
          instance_type=instance_type,
          serializer=serializer,
          deserializer=deserializer,
          accelerator_type=accelerator_type,
          endpoint_name=endpoint_name,
          tags=format_tags(tags),
          kms_key=kms_key,
          wait=wait,
          data_capture_config=data_capture_config,
          async_inference_config=async_inference_config,
          serverless_inference_config=serverless_inference_config,
          volume_size=volume_size,
          model_data_download_timeout=model_data_download_timeout,
          container_startup_health_check_timeout=container_startup_health_check_timeout,
          inference_recommendation_id=inference_recommendation_id,
          explainer_config=explainer_config,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional, List, Dict
  import sagemaker
  from sagemaker import image_uris, s3, ModelMetrics
  from sagemaker.deserializers import JSONDeserializer
  from sagemaker.deprecations import removed_kwargs
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.metadata_properties import MetadataProperties
  from sagemaker.predictor import Predictor
  from sagemaker.serializers import JSONSerializer
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import PipelineSession
  from sagemaker.utils import format_tags
label_desc: Use image node_id 3 for calling the TensorFlowModel obj's underlying member method code with example usage: obj.deploy(initial_instance_count, instance_type, serializer, deserializer, accelerator_type, endpoint_name, tags, kms_key, wait, data_capture_config, async_inference_config, serverless_inference_config, volume_size, model_data_download_timeout, container_startup_health_check_timeout, inference_recommendation_id, explainer_config) and returns: super