}

  std::vector<string> output_tensor_names;
  output_tensor_names.reserve(fetch_outputs.size());
  for (auto const& output : fetch_outputs) {
    output_tensor_names.push_back(output.name());
  }
  std::vector<string> target_node_names;
  target_node_names.reserve(run_outputs.size());
  for (auto const& output : run_outputs) {
    target_node_names.push_back(output.node()->name());

  SmallVector<Value, 4> operands;
  operands.reserve(operands_size);
  operands.append(while_op.getOperands().begin(), while_op.getOperands().end());
  operands.append(extra_operands.begin(), extra_operands.end());
  SmallVector<Type, 4> new_types;
  new_types.reserve(operands_size);
  new_types.append(while_op.getResultTypes().begin(),

  if (type.getElementType().isF32()) {
    tflite::internal::sparsity::FormatConverter<float> format_converter(
        shape, traversal_order, format, b_size, b_map);
    std::vector<float> data;
    data.reserve(type.getNumElements());
    for (const auto val : attr.getValues<float>()) data.push_back(val);
    format_converter.DenseToSparse(data.data());
    sparsity =

  if (output_names) {
    output_names_vec.reserve(noutputs);
    for (int i = 0; i < noutputs; ++i) {
      output_names_vec.push_back(string(output_names[i]));
    }
  }

  // Process control output names.
  std::vector<string> control_output_names_vec;
  if (control_output_names) {
    control_output_names_vec.reserve(ncontrol_outputs);
    for (int i = 0; i < ncontrol_outputs; ++i) {

  std::vector<std::unique_ptr<ParallelTensor>> per_device_outputs;
  per_device_outputs.reserve(first_op_output_count);
  for (int i = 0; i < first_op_output_count; ++i) {
    std::vector<TensorHandlePtr> components;
    components.reserve(underlying_devices_.size());
    for (int j = 0; j < underlying_devices_.size(); ++j) {

                                        OpBuilder &rewriter) {
  auto i64_type = rewriter.getIntegerType(64);
  if (types.size() > 1) {
    llvm::SmallVector<mlir::Attribute> v;
    v.reserve(types.size());
    for (const mlir::Type &t : types) {
      if (mlir::isa<mhlo::TokenType>(t)) continue;
      auto layout = GetTPUInfeedLayout({t}, rewriter);
      if (failed(layout)) return failure();

  // directed edge (i->j) into the graph.
  std::vector<std::vector<uint64_t>> dependency_graph;
  dependency_graph.reserve(n);

  Operation &yield_op = body.back();
  auto results = yield_op.getOperands();
  for (auto i = 0; i < n; ++i) {
    std::vector<uint64_t> neighbors;
    neighbors.reserve(n);
    FindProducers(results[i], neighbors);
    dependency_graph.push_back(neighbors);
  }

				initialObjects = testCtx.listAll(t)
				initialObjects = testCtx.updateAPIServer(t, initialObjects, tc.prepare.reserve)
				status := testCtx.p.Reserve(testCtx.ctx, testCtx.state, tc.pod, selectedNode.Node().Name)
				t.Run("reserve", func(t *testing.T) {
					testCtx.verify(t, tc.want.reserve, initialObjects, nil, status)
				})
				if status.Code() != framework.Success {
					unschedulable = true
				}
			}

  SmallVector<int64_t> new_start;
  SmallVector<int64_t> new_limit;
  SmallVector<int64_t> new_slice_shape;
  new_start.reserve(first.getStrides().size());
  new_limit.reserve(first.getStrides().size());
  new_slice_shape.reserve(first.getStrides().size());

  for (int i = 0; i < first.getStrides().size(); ++i) {
    const int64_t first_start =

	defaultPlugins.Score = mergePluginSet(logger, defaultPlugins.Score, customPlugins.Score)
	defaultPlugins.Reserve = mergePluginSet(logger, defaultPlugins.Reserve, customPlugins.Reserve)
	defaultPlugins.Permit = mergePluginSet(logger, defaultPlugins.Permit, customPlugins.Permit)
	defaultPlugins.PreBind = mergePluginSet(logger, defaultPlugins.PreBind, customPlugins.PreBind)