return
}

// -----

// CHECK-LABEL: handles_all_versions
func.func private @handles_all_versions() {
  // CHECK: "tf.Iterator"
  // CHECK-SAME: 1x42
  %0 = "tf.AnonymousIterator"() {output_shapes = [
    #tf_type.shape<1x42>], output_types = [f32]} : () -> tensor<!tf_type.resource>
  // CHECK: "tf.Iterator"
  // CHECK-SAME: 2x42
  %1, %2 = "tf.AnonymousIteratorV2"() {output_shapes = [

      Name("MakeIterator").Device(DEVICE).HostMemory("dataset"),               \
      data::MakeIteratorOp);                                                   \
  REGISTER_KERNEL_BUILDER(Name("AnonymousIterator").Device(DEVICE),            \
                          data::AnonymousIteratorHandleOp);                    \
  REGISTER_KERNEL_BUILDER(Name("AnonymousIteratorV2").Device(DEVICE),          \

  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<tf_device::TensorFlowDeviceDialect>();
  }

  void runOnOperation() override;
};

// Create the AnonymousIterator for `reduce_dataset` with `dataset_types` using
// `builder`.
AnonymousIteratorV3Op CreateIterator(OpBuilder builder,
                                     llvm::ArrayRef<Type> dataset_types,

}

def NameAnonymousIteratorsPass : Pass<"tf-name-anonymous-iterators", "ModuleOp"> {
  let summary = "Converts anonymous iterators to named iterators";
  let description = [{
      This converts AnonymousIterator ops to Iterator, thus giving them a name.
      For example, this will convert
        %0 = "tf.AnonymousIteratorV3"() {...}
      to
        %0 = "tf.Iterator"() {shared_name = "_iterator1", ...}
  }];

-direction             : Move transposes to the beginning or the end of the block where they are defined.
```
### `-tf-name-anonymous-iterators`

_Converts anonymous iterators to named iterators_

This converts AnonymousIterator ops to Iterator, thus giving them a name.
For example, this will convert
  %0 = "tf.AnonymousIteratorV3"() {...}
to
  %0 = "tf.Iterator"() {shared_name = "_iterator1", ...}
### `-tf-optimize`

    TF_F32OrF64Tensor:$output
  );

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

def TF_AnonymousIteratorOp : TF_Op<"AnonymousIterator", [TF_UniqueResourceAllocation]> {
  let summary = "A container for an iterator resource.";

  let arguments = (ins
    ConfinedAttr<TypeArrayAttr, [ArrayMinCount<1>]>:$output_types,