class_docstr: A model server client for TensorFlow Serving.
  
  It uses gRPC client to talk to TensorFlow Serving server.
code: def __init__(self, endpoint: str, model_name: str):
      # Note that the channel instance is automatically closed (unsubscribed) on
      # deletion, so we don't have to manually close this on __del__.
      self._channel = grpc.insecure_channel(endpoint)
      self._model_name = model_name
      self._model_service = model_service_pb2_grpc.ModelServiceStub(
          self._channel
      )
      self._prediction_service = (
          prediction_service_pb2_grpc.PredictionServiceStub(
              self._channel
          )
      )
  
file_path: tfx/tfx/components/infra_validator/model_server_clients/tensorflow_serving_client.py
imports: from absl import logging
  import grpc
  from tfx.components.infra_validator import types
  from tfx.components.infra_validator.model_server_clients import base_client
  from tensorflow_serving.apis import classification_pb2
  from tensorflow_serving.apis import model_pb2
  from tensorflow_serving.apis import predict_pb2
  from tensorflow_serving.apis import prediction_service_pb2_grpc
  from tensorflow_serving.apis import regression_pb2
label_desc: Use image node_id 1 to create a new TensorFlowServingClient object from inherited base classes: base_client with example: obj = TensorFlowServingClient(endpoint, model_name)