}
  builder_.create<func::ReturnOp>(func_.getLoc(), ret_operands);

  auto arg_types = body.getArgumentTypes();
  auto result_types = body.getTerminator()->getOperandTypes();
  func_.setType(FunctionType::get(func_.getContext(), arg_types, result_types));
  *f = new MlirFunction(std::move(context_), std::move(module_), func_);
  return absl::OkStatus();
}

extern "C" {

void UpdateFuncType(func::FuncOp func) {
  auto arg_types = func.front().getArgumentTypes();
  auto result_types = func.front().getTerminator()->getOperandTypes();
  func.setType(FunctionType::get(func.getContext(), arg_types, result_types));
}

void HandleFuncOp(Operation* op) {
  auto func = llvm::cast<func::FuncOp>(op);
  UpdateFuncType(func);
}

  if (extern_values_passthrough)
    for (auto input : extern_values) return_types.push_back(input.getType());
  if (only_one_return_value) {
    return_types.resize(1);
  }

  auto type = FunctionType::get(region.getContext(), input_types, return_types);

  // Create new function and extract region body into the function.
  auto outlined_func = builder.create<func::FuncOp>(loc, name, type);

    SymbolTableCollection& symbol_table, Operation* op,
    ArrayRef<Attribute> branches,
    llvm::function_ref<std::string(unsigned branch_index)> branch_name) {
  SmallVector<FunctionType, 2> branch_types;
  branch_types.reserve(branches.size());

  if (llvm::any_of(op->getOperands(),
                   [](Value value) { return value == nullptr; }))

void UpdateFuncType(func::FuncOp func) {
  llvm::SmallVector<Type, 8> arg_types;
  for (auto arg : func.getArguments()) arg_types.push_back(arg.getType());
  func.setType(
      FunctionType::get(func.getContext(), arg_types,
                        func.front().getTerminator()->getOperandTypes()));
}

// Holds the size value of a tensor list and whether the size is statically
// known (fixed).

void UpdateFuncType(func::FuncOp func) {
  llvm::SmallVector<Type, 8> arg_types;
  for (auto arg : func.getArguments()) arg_types.push_back(arg.getType());
  func.setType(
      FunctionType::get(func.getContext(), arg_types,
                        func.front().getTerminator()->getOperandTypes()));
}

// Finds the accessed gradient sources for each tensor array argument.

</pre>

<pre>
*Point
*[4]int
</pre>

<h3 id="Function_types">Function types</h3>

<p>
A function type denotes the set of all functions with the same parameter
and result types. The value of an uninitialized variable of function type
is <code>nil</code>.
</p>

<pre class="ebnf">
FunctionType   = "func" Signature .
Signature      = Parameters [ Result ] .

  auto new_argument_types = llvm::to_vector(func_body.getArgumentTypes());
  auto new_result_types =
      llvm::to_vector(func_body.getTerminator()->getOperandTypes());
  func.setType(FunctionType::get(builder.getContext(), new_argument_types,
                                 new_result_types));
}

// Replaces a function terminator `return` with another `return` that has an
// extra `mhlo.token` operand.

</pre>

<pre>
*Point
*[4]int
</pre>

<h3 id="Function_types">Function types</h3>

<p>
A function type denotes the set of all functions with the same parameter
and result types. The value of an uninitialized variable of function type
is <code>nil</code>.
</p>

<pre class="ebnf">
FunctionType   = "func" Signature .
Signature      = Parameters [ Result ] .

      auto scalar = RankedTensorType::get({}, input_type.getElementType());
      expected_callee_input_types.push_back(scalar);
      expected_callee_input_types.push_back(scalar);
    }
    FunctionType expected_callee_type = mlir::FunctionType::get(
        op->getContext(), expected_callee_input_types,
        RankedTensorType::get({}, IntegerType::get(op->getContext(), 1)));
    if (callee_type != expected_callee_type) {