* Introducing `tf.types.experimental.AtomicFunction` as the fastest way to perform TF computations in Python.

    * Can be accessed through `inference_fn` property of `ConcreteFunction`s
    * Does not support gradients.

  let results = (outs
    Res<TF_I32OrI64Tensor, [{A `Tensor` with the same shape as `values`.  It contains the first scalar index
into the last dimension where values can be inserted without changing the
ordered property.}]>:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
  TF_DerivedResultTypeAttr out_type = TF_DerivedResultTypeAttr<0>;
}

def TF_MakeIteratorOp : TF_Op<"MakeIterator", []> {

Class <org.gradle.api.internal.artifacts.repositories.descriptor.FlatDirRepositoryDescriptor$Property> is not annotated (directly or via its package) with @org.gradle.api.NonNullApi in (FlatDirRepositoryDescriptor.java:0)