*   Added `keras.export.ExportArchive` class for low-level customization of the process of exporting SavedModel artifacts for inference. Both ways of exporting models are based on `tf.function` tracing and produce a TF program composed of TF ops. They are meant primarily for environments where the TF runtime is available, but not the Python interpreter, as is typical for production...

Class <org.gradle.api.reporting.components.internal.AbstractBinaryRenderer> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (AbstractBinaryRenderer.java:0)
Class <org.gradle.api.reporting.components.internal.BinaryRenderer> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (BinaryRenderer.java:0)

                        type: object
                      description: Optional.
                      type: object
                    disableSpanReporting:
                      description: Controls span reporting.
                      nullable: true
                      type: boolean
                    match:
                      description: Allows tailoring of behavior to specific conditions.

  );
}

def TF_DynamicEnqueueTPUEmbeddingArbitraryTensorBatchOp : TF_Op<"DynamicEnqueueTPUEmbeddingArbitraryTensorBatch", [SameVariadicOperandSize, TF_TPUEmbeddingWriteEffect]> {
  let summary = [{
Eases the porting of code that uses tf.nn.embedding_lookup_sparse().
  }];

  let description = [{
embedding_indices[i] and aggregation_weights[i] correspond
to the ith feature.

                        type: object
                      description: Optional.
                      type: object
                    disableSpanReporting:
                      description: Controls span reporting.
                      nullable: true
                      type: boolean
                    match:
                      description: Allows tailoring of behavior to specific conditions.

(dataSource and dataSourceRef) will be set to the same value automatically if one of them is empty and the other is non-empty. When namespace is specified in dataSourceRef, dataSource isn't set to the same value and must be empty. There are three important differences between dataSource and dataSourceRef: * While dataSource only allows two specific types of objects, dataSourceRef\n  allows any non-core object, as well as PersistentVolumeClaim objects.\n* While dataSource ignores disallowed values (dropping...