FunctionType::get(func.getContext(), new_input_types,
                        func.front().getTerminator()->getOperandTypes()));
}

// Contains cached information for decomposed callee functions for (stateful)
// partitioned call ops.
struct PartitionedCallStackOpsInfo {
  bool signature_change;
  func::FuncOp decomposed_callee;
  llvm::SmallDenseMap<int64_t, int64_t> stack_var_arg_to_size_arg;
};

std::unique_ptr<OperationPass<ModuleOp>> CreateTPUResourceReadForWritePass();

// Creates a pass that reorders partitiioned resource reads and replicated
// inputs.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateTPUReorderReplicateAndPartitionedInputsPass();

// Creates a pass that partitions unpartitioned resource read/write to
// partitioned resource variables.
std::unique_ptr<OperationPass<func::FuncOp>>

pkg net/http, func ParseCookie(string) ([]*Cookie, error) #66008
pkg net/http, func ParseSetCookie(string) (*Cookie, error) #66008
pkg net/http, method (*Request) CookiesNamed(string) []*Cookie #61472
pkg net/http, type Cookie struct, Partitioned bool #62490
pkg net/http, type Cookie struct, Quoted bool #46443
pkg net/http, type Request struct, Pattern string #66405

// in this function should be on the same host.
struct FunctionMetadata {
  // The original function name before partition.
  llvm::StringRef original_name;
  // The insertion point of partition functions.
  Block::iterator insertion_point;
  // The partitioned function name.
  llvm::StringRef partition_name;
  // The input values of the function.
  llvm::SmallVector<Value, 4> inputs;

  let summary =
    "returns `f(inputs)`, where `f`'s body is placed and partitioned.";

  let description = [{
Asynchronously executes a function, potentially across multiple devices but
within a single process. The kernel places and partitions a given function's
underlying graph, and executes each of the partitioned subgraphs as a function.
  }];

  let arguments = (ins
    Variadic<TF_Tensor>:$args,

func.func @cluster_func(%arg0: tensor<*xi32>) -> tensor<*xi32> {
  func.return %arg0 : tensor<*xi32>
}

// -----

// Tests output sharding of unpartitioned resource write takes on same sharding
// as unpartitioned resource.

// CHECK-LABEL: func @partitioned_input_output
func.func @partitioned_input_output(%arg0: tensor<!tf_type.resource<tensor<f32>>>) {

// 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