syscallingP: make(map[ProcID]GoID),
		syscallingG: make(map[GoID]ProcID),
		rangesP:     make(map[rangeP]GoID),
	}
}

type rangeP struct {
	id   ProcID
	name string
}

// Event feeds a single event into the stats summarizer.
func (s *Summarizer) Event(ev *Event) {
	if s.syncTs == 0 {
		s.syncTs = ev.Time()
	}
	s.lastTs = ev.Time()

	switch ev.Kind() {

}

TEST(TFCompileTest, LookupNameIndex) {
  // add doesn't have any names defined in its config.
  AddComp add;
  EXPECT_FALSE(add.HasNameIndices());

  // muladd has names defined for all feeds and fetches.
  ::foo::bar::MatMulAndAddComp muladd;
  EXPECT_TRUE(muladd.HasNameIndices());

  EXPECT_EQ(muladd.LookupArgIndex("x"), 0);
  EXPECT_EQ(muladd.LookupArgIndex("y"), 1);

    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;

    op. The order of ops will be preserved. Functions named `main` with no
    `tf.entry_function` attribute will not be pruned, as such graphs/functions may
    have been imported from a V1 TensorFlow graph, where feeds/fetches/targets are
    not provided at certain stages of IR transformation (e.g. pre-placement).

    Option `ops-to-preserve` allows to specify ops that should not be pruned,
    regardless of their reachability.

specific language governing permissions and limitations
under the License.
-->

<project>

  <extend>../project-which-needs-directory-alignment.xml</extend>

  <parent>
    <groupId>maven</groupId>
    <artifactId>project-which-needs-directory-alignment</artifactId>
    <version>1.0-beta-9</version>
  </parent>

specific language governing permissions and limitations
under the License.
-->

<project>

  <extend>project-which-needs-directory-alignment.xml</extend>

  <parent>
    <groupId>maven</groupId>
    <artifactId>project-which-needs-directory-alignment.xml</artifactId>
    <version>1.0-beta-9</version>
  </parent>

  <groupId>maven</groupId>
  <artifactId>maven-foo</artifactId>

_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