}

TEST_F(CApiColocationTest, Proto_ColocateWith) {
  SetViaProto(desc_, {"loc:@feed2"});
  TF_ColocateWith(desc_, feed1_);
  FinishAndVerify(desc_, {"loc:@feed1", "loc:@feed2"});
}

TEST_F(CApiColocationTest, ColocateWith_ColocateWith) {
  TF_ColocateWith(desc_, feed1_);
  TF_ColocateWith(desc_, feed2_);
  FinishAndVerify(desc_, {"loc:@feed1", "loc:@feed2"});
}

    const TF_Operation* const* target_opers, int ntargets,
    // RunMetadata
    TF_Buffer* run_metadata,
    // Output status
    TF_Status*);

// Set up the graph with the intended feeds (inputs) and fetches (outputs) for a
// sequence of partial run calls.
//
// On success, returns a handle that is used for subsequent PRun calls. The

   */
  // Define
  TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");
  TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");
  TF_Operation* feed3 = Placeholder(func_graph_, s_, "feed3");
  TF_Operation* add1 = Add(feed1, feed2, func_graph_, s_, "add1");
  TF_Operation* add2 = Add(add1, feed3, func_graph_, s_, "add2");
  Define(-1, {}, {feed1, feed2, feed3}, {add1, add2}, {});

  // Use, run, and verify

}

// TODO(b/177675373): Make dtypes and shapes derived attributes,
// use more general solution.
def TF_InfeedEnqueueTupleOp : TF_Op<"InfeedEnqueueTuple", []> {
  let summary = [{
Feeds multiple Tensor values into the computation as an XLA tuple.
  }];

  let arguments = (ins
    Arg<Variadic<TF_Tensor>, [{A list of tensors that will be provided using the infeed mechanism.}]>:$inputs,

        UpdateReplicationAttr(&op, new_repl_info);
      }
    }
    return LogicalResult::success();
  }

  // After inlining, there will likely be some instances where a
  // TPUReplicatedInput feeds directly into a TPUReplicatedOutput. Find such
  // pairs and remove them.
  LogicalResult RemoveOutputInputPairs(func::FuncOp func) {
    llvm::SetVector<Operation*> ops_to_erase;

_Decompose stack operations into local variable operations. Needs static shapes._

A pass that converts stack operations to tensor operations and read/assign
ops on local variables. A later resource lifting pass can further remove the
local variables.

This pass requires that the full shape of the stack can be inferred: 1) the
maximum size needs to be a constant and 2) a push op can be found with a

  /// DEFAULT IMPLEMENTATION: Gets statistics about `path`. Needs `stat`.
  int64_t (*get_file_size)(const TF_Filesystem* filesystem, const char* path,
                           TF_Status* status);

  /// Translates `uri` to a filename for the filesystem
  ///
  /// A filesystem is registered for a specific scheme and all of the methods
  /// should work with URIs. Hence, each filesystem needs to be able to

		finalDelay       time.Duration
		wantRequestCount int
	}{
		{
			name:        "three adds with no batching",
			batchPeriod: 0 * time.Second,
			adds: []podAdd{
				{
					// endpoints.Run needs ~100 ms to start processing updates.
					delay: 200 * time.Millisecond,
				},
				{
					delay: 100 * time.Millisecond,
				},
				{
					delay: 100 * time.Millisecond,
				},
			},

		wantRequestCount int
	}{
		{
			name:        "three updates with no batching",
			batchPeriod: 0 * time.Second,
			podsCount:   10,
			updates: []podUpdate{
				{
					// endpoints.Run needs ~100 ms to start processing updates.
					delay:   200 * time.Millisecond,
					podName: "pod0",
					podIP:   "10.0.0.0",
				},
				{
					delay:   100 * time.Millisecond,
					podName: "pod1",

		// class for objects which have reduced quorum
		// storage class only needs to be honored for
		// Read() requests alone which we already do.
		writeQuorums[i] = len(storageDisks)/2 + 1
	}

	versionsMap := make(map[string]FileInfoVersions, len(objects))
	for i := range objects {
		// Construct the FileInfo data that needs to be preserved on the disk.
		vr := FileInfo{