class_docstr: A ``FrameworkModel`` implementation for inference with TensorFlow Serving.
docstr: Placeholder docstring.
docstr_tok: ["Placeholder","docstring","."]
code: def _get_image_uri(
      self,
      instance_type,
      accelerator_type=None,
      region_name=None,
      serverless_inference_config=None,
  ):
      """Placeholder docstring."""
      if self.image_uri:
          return self.image_uri
  
      logger.info(
          "image_uri is not presented, retrieving image_uri based on instance_type, "
          "framework etc."
      )
      return image_uris.retrieve(
          self._framework_name,
          region_name or self.sagemaker_session.boto_region_name,
          version=self.inference_framework_version,
          instance_type=instance_type,
          accelerator_type=accelerator_type,
          image_scope="inference",
          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 7 for calling the TensorFlowModel obj's underlying member method code with example usage: obj._get_image_uri(instance_type, accelerator_type, region_name, serverless_inference_config) and returns: image_uris, self