class_docstr: A ``FrameworkModel`` implementation for inference with TensorFlow Serving.
docstr: Create a URI for the serving image.
  
  Args:
      region_name (str): AWS region where the image is uploaded.
      instance_type (str): SageMaker instance type. Used to determine device type
          (cpu/gpu/family-specific optimized).
      accelerator_type (str): The Elastic Inference accelerator type to
          deploy to the instance for loading and making inferences to the
          model (default: None). For example, 'ml.eia1.medium'.
      serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
          Specifies configuration related to serverless endpoint. Instance type is
          not provided in serverless inference. So this is used to determine device type.
  
  Returns:
      str: The appropriate image URI based on the given parameters.
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code: def serving_image_uri(
      self,
      region_name,
      instance_type,
      accelerator_type=None,
      serverless_inference_config=None,
  ):  # pylint: disable=unused-argument
      """Create a URI for the serving image.
  
      Args:
          region_name (str): AWS region where the image is uploaded.
          instance_type (str): SageMaker instance type. Used to determine device type
              (cpu/gpu/family-specific optimized).
          accelerator_type (str): The Elastic Inference accelerator type to
              deploy to the instance for loading and making inferences to the
              model (default: None). For example, 'ml.eia1.medium'.
          serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
              Specifies configuration related to serverless endpoint. Instance type is
              not provided in serverless inference. So this is used to determine device type.
  
      Returns:
          str: The appropriate image URI based on the given parameters.
  
      """
      return self._get_image_uri(
          instance_type=instance_type,
          accelerator_type=accelerator_type,
          region_name=region_name,
          serverless_inference_config=serverless_inference_config,
      )
  
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 8 for calling the TensorFlowModel obj's underlying member method code with example usage: obj.serving_image_uri(region_name, instance_type, accelerator_type, serverless_inference_config) and returns: self