Tensorflow: Cannot create non-scalar Tensors from arrays of boxed values

Question

I am following the instructions provided below to load a trained Tensorflow model in Java:

How to import an saved Tensorflow model train using tf.estimator and predict on input data

I successfully saved my python model and imported it in my java code.

Here is the portion of my python code which saves my model:

# Export trained model
def serving_input_receiver_fn():
  inputs = {"my_feature": tf.placeholder(shape=[None, 1], dtype=tf.float32)}
  return tf.estimator.export.ServingInputReceiver(inputs, inputs)

export_dir = kmeans.export_savedmodel(
    export_dir_base="/tmp/path_to_model_directory",
    serving_input_receiver_fn=serving_input_receiver_fn,
    assets_extra={"file_name":"/tmp/path_to_file"
                 })

And here is the java code for reading the model, making a prediction and reading the result:

Tensor x = Tensor.create(
            new long[] {1, 1},
            FloatBuffer.wrap(
                new float[] {
                  10000.0f
                }));

final String xName = "inputs_tensor_info_name";
final String yName = "outputs_tensor_info_name";


List<Tensor<?>> outputTensor = session.runner()
        .feed(xName, x)
        .fetch(yName)
        .run();

for (int i=0; i < outputTensor.size(); i++ ) {  
    Integer[] copy = new Integer[1];
    System.out.println(outputTensor.get(i));
    System.out.println(outputTensor.get(i).copyTo(copy));
}

However when trying to load the prediction in an array the following exception occurs:

Exception in thread "main" java.lang.IllegalArgumentException: cannot create non-scalar Tensors from arrays of boxed values
at org.tensorflow.Tensor.objectCompatWithType(Tensor.java:722)
at org.tensorflow.Tensor.throwExceptionIfTypeIsIncompatible(Tensor.java:742)
at org.tensorflow.Tensor.copyTo(Tensor.java:450)
at com.pivot.maven.quickstart.HelloTF.main(HelloTF.java:105)

Having failed to figure out how to go about this, I would appreciate if someone pointed me in the right direction. Thank you.