return %pi : !rtype
}
```
### `-tf-tpu-resource-partition`

_Partitions unpartitioned resource read/write to partitioned resource variables._

This pass creates individual resource reads/writes from the unpartitioned
resource variable (from `tf.TPUPartitionedInput`) to individual partitioned
resource variables (`tf.TPUPartitionedInput` operands). As resource op

  }];

  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(),

// CHECK-SAME:   topology = "topology"
// CHECK:        tf_device.return %[[CLUSTER]]#0, %[[CLUSTER]]#1
// CHECK:      return %[[REPLICATE]]#0, %[[REPLICATE]]#3


// Test replication with model parallelism using partitioned resource inputs.
// The cluster will be wrapped in a `tf_device.cluster` first and then by a
// replicate.
// TPUPartitionedInputV2 nodes would be inside the replicate but outside the
// cluster.

message RollingUpdateStatefulSetStrategy {
  // Partition indicates the ordinal at which the StatefulSet should be partitioned
  // for updates. During a rolling update, all pods from ordinal Replicas-1 to
  // Partition are updated. All pods from ordinal Partition-1 to 0 remain untouched.
  // This is helpful in being able to do a canary based deployment. The default value is 0.
  optional int32 partition = 1;

      This transformation pass optimizes the op execution order of the ops in
      the model.
  }];
  let constructor = "CreateOptimizeOpOrderPass()";
}

def PartitionedTopologicalSortPass : Pass<"tfl-partitioned-topological-sort", "mlir::func::FuncOp"> {
  let summary = "Re-sort execution order such that delegated ops stay together";
  let constructor = "CreatePartitionedTopologicalSortPass()";
    let description = [{