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);

// the in-loop adjustment is 5 (for example), then there is not much
// point treating it as inlinable. On the other hand "bar" has a param
// property (parameter "x" feeds unmodified to an "if" statement") and
// a return property (always returns same constant) meaning that a
// given call _could_ be rescored down as much as -35 points-- thus if

    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

}

// 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.

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.

  <mime-type type="text/vnd.in3d.spot">
    <glob pattern="*.spot"/>
  </mime-type>
  <mime-type type="text/vnd.iptc.anpa">
    <acronym>ANPA</acronym>
    <_comment>American Newspaper Publishers Association Wire Feeds</_comment>
    <glob pattern="*.anpa"/>
    <magic priority="50">
      <match value="\x16\x16\x01" type="string" offset="0"/>
    </magic>
  </mime-type>
  <mime-type type="text/vnd.iptc.newsml"/>

        (command `print_source` / `ps`)
    *   New navigation bar in Curses-based UI
    *   NodeStepper (command `invoke_stepper`) now uses intermediate tensor
        dumps. It also uses `TensorHandles` as direct feeds during successive
        `cont` calls for improved performance and reduced memory consumption.
*   Initial release of installation guides for Java, C, and Go.
*   Added Text Dashboard to TensorBoard.