ConversionPatternRewriter *rewriter) const {
    auto guard = OpBuilder::InsertionGuard(*rewriter);
    auto inputs = branch_func.getFunctionType().getInputs();
    Block *block = rewriter->createBlock(
        &branch_func.getBody(), branch_func.begin(), inputs,
        SmallVector<Location>(inputs.size(), branch_func.getLoc()));

    if (mlir::isa<VariantType>(getElementTypeOrSelf(addn_op.getType())))
      return failure();
    llvm::SmallVector<Value, 4> operands(addn_op.getInputs().begin(),
                                         addn_op.getInputs().end());

    int64_t n = operands.size();
    // Keep doing tree-based reduction when there are more than one operand.
    while (n > 1) {

void AddCallOpInWhileOpRegion(mlir::Region& region, mlir::func::FuncOp func) {
  OpBuilder op_builder{region};
  region.push_back(new mlir::Block());
  Location loc = region.getLoc();
  auto inputs = func.getFunctionType().getInputs();
  region.addArguments(inputs, mlir::SmallVector<Location>(inputs.size(), loc));
  op_builder.setInsertionPointToStart(&region.front());
  auto call_op = op_builder.create<mlir::func::CallOp>(

  auto new_return =
      builder.create<func::ReturnOp>(old_return->getLoc(), old_and_new_retvals);
  old_return->erase();
  callee.setType(FunctionType::get(
      callee.getContext(), callee.getFunctionType().getInputs(),
      llvm::to_vector<4>(new_return.getOperandTypes())));
  return success();
}

// Updates a PartitionedCallOp/StatefulPartitionedCallOp according to the

This runtime API is provided through a couple of aptly named properties that are available on every Gradle task:

* link:{groovyDslPath}/org.gradle.api.Task.html#org.gradle.api.Task:inputs[Task.getInputs()] of type link:{javadocPath}/org/gradle/api/tasks/TaskInputs.html[TaskInputs]

Method <org.gradle.api.DefaultTask.getInputs()> has arguments/return type org.gradle.api.internal.TaskInputsInternal that is not Gradle public API or primitive or built-in JDK classes or Kotlin classes in (DefaultTask.java:0)

  // Create a session for this graph.
  CSession csession(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Run the graph.
  csession.SetInputs({{feed, Int32Tensor(3)}});
  csession.SetOutputs({add});
  csession.Run(s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_Tensor* out = csession.output_tensor(0);
  ASSERT_TRUE(out != nullptr);

    // Create a session for this graph
    CSession csession(host_graph_, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

    // Run
    csession.SetInputs(inputs);
    csession.SetOutputs(outputs);
    csession.Run(s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

    // Check results
    for (int i = 0; i < expected_results.size(); ++i) {