for (Value result : subgraph.FuncOutputs()) {
    Value cloned_output = values_in_scope.lookup(result);
    return_operands.push_back(cloned_output);
  }
  function_builder.create<mlir::func::ReturnOp>(new_func.getLoc(),
                                                return_operands);
  ops_added.func_op = new_func;
  module.push_back(new_func);
  return new_func;
}

  std::unique_ptr<llvm::MemoryBuffer> mem_buffer;
  llvm::StringRef quantized_function_library =
      GetFunctionLibrary(quantization_method_, op_set_);

  if (quantized_function_library.empty()) {
    emitError(module.getLoc())
        << "Failed to get function library for the opset.";
    signalPassFailure();
    return;
  }

  mem_buffer =
      llvm::MemoryBuffer::getMemBuffer(quantized_function_library,

  builder_.setInsertionPointAfter(op);
  const Value original_result = op->getResult(result_index);
  QuantizeValue(original_result, quantized_type, op->getLoc());
}

void QuantizationDriver::QuantizeArg(BlockArgument arg,
                                     const QuantizedType quantized_type) {
  builder_.setInsertionPointToStart(arg.getOwner());

    indices_type = RankedTensorType::get(new_start_indices_shape,
                                         indices_type.getElementType());
    indices = rewriter.create<mhlo::ReshapeOp>(parent_op->getLoc(),
                                               indices_type, indices);
  } else if (index_vector_dim != indices_type.getRank() - 1) {
    // If index_vector_dim isn't the last dimension in indices then it isn't

func (c *sigctxt) oldmask() uint32 { return 0 }

func (c *sigctxt) sigcode() uint32 { return uint32(c.info.si_code) }
func (c *sigctxt) sigaddr() uint32 { return uint32(c.info.si_addr) }

func (c *sigctxt) set_pc(x uint32)  { c.regs().__gregs[15] = x }
func (c *sigctxt) set_sp(x uint32)  { c.regs().__gregs[13] = x }
func (c *sigctxt) set_lr(x uint32)  { c.regs().__gregs[14] = x }

func (c *sigctxt) oldmask() uint32 { return c.regs().oldmask }

func (c *sigctxt) sigcode() uint32 { return uint32(c.info.si_code) }
func (c *sigctxt) sigaddr() uint32 { return c.info.si_addr }

func (c *sigctxt) set_pc(x uint32)  { c.regs().pc = x }
func (c *sigctxt) set_sp(x uint32)  { c.regs().sp = x }
func (c *sigctxt) set_lr(x uint32)  { c.regs().lr = x }
func (c *sigctxt) set_r10(x uint32) { c.regs().r10 = x }

                                                    TypeRange(types), result);
      new_forward_ops.insert(replicated_output);
      // TODO(bfontain): Check for other attributes.
      replicated_output->setAttr(kDevice, builder.getStringAttr(""));
      TF::TPUReplicatedInputOp input = builder.create<TF::TPUReplicatedInputOp>(

    // Create the TF op
    OperationState op_state(op.getLoc(), tf_op_full_name);
    op_state.addOperands(op.getOperands());
    op_state.addTypes(op.getResultTypes());

    SmallVector<NamedAttribute, 2> attrs;
    std::string parsed_op_name;
    tensorflow::NodeDef node_def;
    if (failed(ParseCustomOption(op.getCustomOption().getValue(), op.getLoc(),
                                 parsed_op_name, attrs, node_def))) {

func (c *sigctxt) oldmask() uint32 { return 0 }

func (c *sigctxt) sigcode() uint32 { return uint32(c.info._code) }
func (c *sigctxt) sigaddr() uint32 { return uint32(c.info._reason) }

func (c *sigctxt) set_pc(x uint32)  { c.regs().__gregs[_REG_R15] = x }
func (c *sigctxt) set_sp(x uint32)  { c.regs().__gregs[_REG_R13] = x }
func (c *sigctxt) set_lr(x uint32)  { c.regs().__gregs[_REG_R14] = x }

           py::return_value_policy::reference)
      .def("end", &mlir::Block::end)
      .def("addArgument", [](mlir::Block& block, mlir::Type type) {
        return block.addArgument(type, block.getParent()->getLoc());
      });

  py::class_<mlir::Value>(m, "Value").def("getType", &mlir::Value::getType);
  py::class_<mlir::OpResult, mlir::Value>(m, "OpResult");
  py::class_<mlir::BlockArgument, mlir::Value>(m, "BlockArgument");