class_docstr: TensorFlow Serving binary.
class_docstr_tok: ["TensorFlow","Serving","binary","."]
code: def __init__(
      self,
      model_name: str,
      image_name: Optional[str] = None,
      tag: Optional[str] = None,
      digest: Optional[str] = None,
  ):
      super().__init__()
      self._model_name = model_name
      if (tag is None) == (digest is None):
          raise ValueError(
              "Exactly one of `tag` or `digest` should be used."
          )
      image_name = image_name or self._DEFAULT_IMAGE_NAME
      if tag is not None:
          self._image = "{}:{}".format(image_name, tag)
      else:
          self._image = "{}@{}".format(image_name, digest)
  
imports: import abc
  import os
  from typing import Any, Dict, List, Optional
  from docker import types
  from tfx.components.infra_validator.model_server_clients import base_client
  from tfx.components.infra_validator.model_server_clients import tensorflow_serving_client
  from tfx.proto import infra_validator_pb2
  from tfx.utils.model_paths import tf_serving_flavor
label_desc: Use image node_id 1 to create a new TensorFlowServing object from inherited base classes: ServingBinary with example: obj = TensorFlowServing(model_name, image_name, tag, digest)