// which returns a dict of string key -> EITHER:
// 1. python callable (taking a checkpoint key) returning SaveableObject OR
// 2. variable (partitioned/resource/reference or otherwise)
// https://github.com/tensorflow/tensorflow/blob/ddc1bbad3dfd4a089eb96014f26cc16664b1b2f8/tensorflow/python/training/saving/saveable_object.py#L58.

def TPUResourceReadsWritesPartitioningPass : Pass<"tf-tpu-resource-partition", "mlir::func::FuncOp"> {
  let summary = "Partitions unpartitioned resource read/write to partitioned resource variables.";

  let description = [{
    This pass creates individual resource reads/writes from the unpartitioned
    resource variable (from `tf.TPUPartitionedInput`) to individual partitioned

   * <p><b>Note:</b> if {@code iterable} is a {@link List}, use {@link Lists#partition(List, int)}
   * instead.
   *
   * @param iterable the iterable to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterable of unmodifiable lists containing the elements of {@code iterable} divided
   *     into partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive

   * <p><b>Note:</b> if {@code iterable} is a {@link List}, use {@link Lists#partition(List, int)}
   * instead.
   *
   * @param iterable the iterable to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterable of unmodifiable lists containing the elements of {@code iterable} divided
   *     into partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive

  }];

  let constructor = "tensorflow::tf2xla::internal::CreateExtractOutsideCompilationPass()";
}

def XlaClusterFormationPass : Pass<"tf-xla-cluster-formation", "ModuleOp"> {
  let summary = "Encapsulate partitioned calls within a Cluster op";
  let description = [{
    This pass clusters `tf.PartitionedCall` and `tf.StatefulPartitionedCall`
    with `_xla_compile_device_type` attribute into a `tf_device.cluster`.

//
// Each call to Session::Run() that identifies a new subgraph (based on feeds
// and fetches) creates some datastructures that live as long as the session
// (the partitioned graph, associated executors etc.).
//
// A pathological case of this would be if say the initialization op
// (main_op/legacy_init_op) involves the use of a large constant. Then we

  return llvm::formatv("requires attribute '{0}'", attribute).str();
}

// Returns nullptr if the op does not have a sharding attribute.
template <typename PartitionedOp>
mlir::Operation* NullUnlessSharded(PartitionedOp op) {
  return op.get_XlaSharding() ? op : nullptr;
}

// Returns a TPUPartitionedInput op connected to a `tf_device.cluster_func`

   * OutOfMemoryError}.
   *
   * @param iterator the iterator to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterator of immutable lists containing the elements of {@code iterator} divided into
   *     partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive
   */

   * OutOfMemoryError}.
   *
   * @param iterator the iterator to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterator of immutable lists containing the elements of {@code iterator} divided into
   *     partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive
   */

  }

  // In theory we can use PartitionedCall if the XLA cluster does not have any
  // stateful operations.  However, for now we choose to be conservative since
  // we don't have any evidence that choosing a stateless partitioned call helps
  // for performance.
  ops::StatefulPartitionedCall call(
      root.WithOpName("stateful_partitioned_call"), args, n->output_types(),