Client-side migration from gRPC beta-API to GA-API - python-3.6

Question

I'm communicating with a tensorflow model server via gRPC in order to get predictions on my data. The python3 app used to establish the connection is based on a tutorial and does its job successfully.
However, the tutorial code uses the python gRPC's beta API and I am trying to be as up-to-date as possible. What's the Python gRPC GA API's equivalent piece of code to establish a secure connection to a model server and to perform a prediction?
Here's the code that does work using dummy data
# Requirements:
# tensorflow-serving-api==1.9.0
# tensorflow==1.11.0
import os
port = os.getenv("PORT")
host = os.getenv("HOST")
cert = os.getenv("CERT") # SSL certificate
token = os.getenv("TOKEN") # Authorization token
from grpc.beta import implementations as grpc_beta_impl
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
creds = grpc_beta_impl.ssl_channel_credentials(root_certificates=cert.encode())
channel = grpc_beta_impl.secure_channel(host, int(port), creds)
stub = prediction_service_pb2.beta_create_PredictionService_stub(
channel,
metadata_transformer=lambda metadata: tuple(metadata) + (("authorization", "Bearer " + token),))
data = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
request = predict_pb2.PredictRequest()
request.model_spec.name = "housing"
request.model_spec.signature_name = 'serving_default'
tfutil = tf.contrib.util
t_proto = tfutil.make_tensor_proto(data,
shape=[1, len(data)],
dtype="float")
request.inputs["input_data"].CopyFrom(t_proto)
pred = stub.Predict(request, 1500)
price = pred.outputs['dense_5/BiasAdd:0'].float_val[0]
print(str(price))
I've tried substituting the the beta API calls with their GA API equivalent. However, there's no such thing as a metadata_transformer in the stub anymore and neither do I understand its purpose nor do I know where to stuff it.
Here's the pertinent piece of code followed by the error message which now requires tf-serving version 1.11.
# Requirements tensorflow-serving==1.11.0
# ... load envvars. Same as before
import grpc
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
creds = grpc.ssl_channel_credentials(root_certificates=cert.encode())
channel = grpc.secure_channel(host + ":" + port, creds)
stub = prediction_service_pb2_grpc.PredictionServiceStub(
channel)
# ... Same as above
Error: <_Rendezvous of RPC that terminated with:
status = StatusCode.UNAUTHENTICATED
details = "no authorization metadata found"
debug_error_string = "{"created":"#1567680306.923100899","description":"Error received from peer ipv4:xx.xxx.xxx.xx:443","file":"src/core/lib/surface/call.cc","file_line":1052,"grpc_message":"no authorization metadata found","grpc_status":16}"
>
I don't have any control over the model server and I don't know whether what I'm attempting is even possible (i.e. is there a server-side pendant of the python gRPC Beta API which must implement the GA API in order for me to succeed?)
Any help is greatly appreciated.