How to pass a *serialized* tensor to a TensorFlow Model Serving server?

Question

I implemented a simple TF model. The model received a serialized tensor of a gray image (simply a 2d ndarray), and restored it to a 2-d tensor. After then, some inference is applied on this 2-d tensor.

I deployed the mode with TensorFlow Model Serving, and tried to send a JSON string to the REST port as follows:

  {
    "instances": [
                    {"b64": bin_str},
                 ]
  }

I tried something like tf.io.serialize_tensor etc to convert input image into a serialized tensor and to pass it to the server, but all failed.

I would like to know how to send a serialized tensor to the serving server.

And my saved model has following signature:

 signatures = {
        "serving_default": _get_serve_tf_examples_fn(
            model,
            transform_output).get_concrete_function(
                # explicitly specify input signature of serve_tf_examples_fn
                tf.TensorSpec(
                    shape=[None],
                    dtype=tf.string,
                    name="examples")),
    }

and the definition of _get_serve_tf_examples_fn is,

def _get_serve_tf_examples_fn(model: tf.keras.models.Model,
                              transform_output: tft.TFTransformOutput):
    # get the Transform graph from the component
    model.tft_layer = transform_output.transform_features_layer()

    @tf.function
    def serve_tf_examples_fn(serialized: str) -> Dict:
        '''  Args: serialized: is serialized image tensor.
        '''
        feature_spec = transform_output.raw_feature_spec()
        # remove label spec.
        feature_spec.pop("label")

        # Deserialize the image tensor.
        parsed_features = tf.io.parse_example(
            serialized,
            feature_spec)

        # Preprocess the example using outputs of Transform pipeline.
        transformed_features = model.tft_layer(parsed_features)
        outputs = model(transformed_features)
        return {"outputs": outputs}

    return serve_tf_examples_fn

The above code segment received a serialized tensor of a gray image (simply a 2d ndarray), and restored it to a 2-d tensor. After then, the model is doing inference on this 2-d tensor.

I would like to know how to send a serialized tensor to the REST port of the serving server.

Any help would be appreciated.