Consume TensorFlowInferSchema from Java Spark 2.4.0

This is not an issue and is more of a request for guidance. I'd like to just use the TensorFlowInferSchema functionality in my Java spark job. Below is the sample snippet

JavaRDD<Example> exampleRdd =
          jsc.parallelize(
              Arrays.asList(
                  Example.newBuilder()
                      .setFeatures(
                          Features.newBuilder()
                              .putFeature(
                                  "Test",
                                  Feature.newBuilder()
                                      .setInt64List(Int64List.newBuilder().addValue(10).build())
                                      .build())
                              .build())
                      .build()));
StructType schema = TensorFlowInferSchema.apply(rdd.rdd(), ???);

However, the apply method expects a second argument - implicit evidence$1 : scala.reflect.runtime.universe.TypeTag[T].

Below is the compiled Java class

def apply[T](rdd : org.apache.spark.rdd.RDD[T])(implicit evidence$1 : scala.reflect.runtime.universe.TypeTag[T]) : org.apache.spark.sql.types.StructType = { /* compiled code */ }

I'm not familiar with Scala and it appears that it's not possible to create TypeTag[Example] in java.

Appreciate if you could share your thoughts on the below

• Is it safe (and possible) to just consume TensorFlowInferSchema in Java without going via spark.read.format("tfrecord").option("recordType", "Example")?
• What to pass for the TypeTag[T] argument?
• Is there a Java example for this use case

Thanks for maintaining this project and highly appreciate your help!

The entry point mentioned is TensorFlowInferSchema.apply, with the Java Spark snippet and compiled signature as the starting context. Review how the TypeTag parameter and spark.read.format("tfrecord") path are exposed, then document a verified Java usage example answering the three questions; done means the guidance is complete and runnable.