#define FUNC1(a, b, c, d, e) \
	MOVL	d, DI; \
	XORL	c, DI; \
	ANDL	b, DI; \
	XORL	d, DI

#define FUNC2(a, b, c, d, e) \
	MOVL	b, DI; \
	XORL	c, DI; \
	XORL	d, DI

#define FUNC3(a, b, c, d, e) \
	MOVL	b, SI; \
	ORL	c, SI; \
	ANDL	d, SI; \
	MOVL	b, DI; \
	ANDL	c, DI; \
	ORL	SI, DI

#define FUNC4 FUNC2

#define MIX(a, b, c, d, e, const) \
	ROLL	$30, b; \
	ADDL	DI, e; \
	MOVL	a, SI; \

      /*function_aliases=*/{{"func1", "alias1"}, {"func2", "alias2"}},
      /*asset_file_defs=*/{});
  ASSERT_THAT(exported_model.function_aliases(), SizeIs(2));
  EXPECT_TRUE(exported_model.function_aliases().contains("func1"));
  EXPECT_THAT(exported_model.function_aliases().at("func1"), StrEq("alias1"));
  EXPECT_TRUE(exported_model.function_aliases().contains("func2"));

    mlir::SymbolRefAttr func = op.getComputation();
    auto func_op = cast<mlir::func::FuncOp>(SymbolTable::lookupSymbolIn(
        op->getParentOfType<mlir::ModuleOp>(), func));
    auto func_ty = func_op.getFunctionType();
    BuildBodyWithCall(rewriter, loc, func, func_ty,
                      &reduce_window_op.getBody());

    rewriter.replaceOp(op, reduce_window_op.getResults());

  // Quantizer via the tf_quant.composite_function UnitAttr attached to
  // func ops. We remove this attribute as this interferes with VHLO conversion.
  // Remove this temporary hack.
  for (auto func_op : module_op.getOps<func::FuncOp>()) {
    func_op->removeAttr(kFusedFunctionAttr);
  }
}

}  // namespace

           operations.back().hasTrait<OpTrait::IsTerminator>())) {
        to_remove.push_back(init_func_op);
      } else {
        to_keep.push_back(sym_ref);
      }
    }

    for (auto func_op : to_remove) rewriter.eraseOp(func_op);

    if (to_keep.empty())
      rewriter.eraseOp(op);
    else
      op->setAttr("initializers", rewriter.getArrayAttr(to_keep));

    return success();
  }
};

// a CallOp. Pass these back to the caller for setting more specific attributes
// after graph mutation has taken place.
struct OpsAdded {
  mlir::func::FuncOp func_op;
  mlir::func::CallOp call_op;
};

// Given a `Subgraph` containing a sequence of adjacent `Operations` from
// the `module`, raise these `Operations` (and any ops contained nested within)

				// and copy all variables
				new := make([]*Var, len(in))
				copy(new, out)
				out = new
				copied = true
			}
			out[i] = w
		}
	}
	return
}

func (subst *subster) func_(f *Func) *Func {
	if f != nil {
		if typ := subst.typ(f.typ); typ != f.typ {
			return substFunc(f, typ)
		}
	}
	return f
}

func substFunc(f *Func, typ Type) *Func {
	copy := *f

				// and copy all variables
				new := make([]*Var, len(in))
				copy(new, out)
				out = new
				copied = true
			}
			out[i] = w
		}
	}
	return
}

func (subst *subster) func_(f *Func) *Func {
	if f != nil {
		if typ := subst.typ(f.typ); typ != f.typ {
			return substFunc(f, typ)
		}
	}
	return f
}

func substFunc(f *Func, typ Type) *Func {
	copy := *f

  TF_ASSERT_OK_AND_ASSIGN(TupleOp root_tuple,
                          ImportXlaComputationIntoModule(computation));
  EXPECT_TRUE(root_tuple);

  int num_func_ops = 0;
  module_->walk([&num_func_ops](func::FuncOp func_op) { num_func_ops++; });

  // Ensure that only a single computation was imported.
  EXPECT_EQ(num_func_ops, 1);
}

TEST_F(Tf2XlaRewriterTest, InsertsConstantParameters) {

  std::vector<Type> output_types = GetValueTypes(outputs);
  builder.setInsertionPointToEnd(&module.getBodyRegion().back());
  func::FuncOp func_op = builder.create<func::FuncOp>(
      module.getLoc(), name,
      builder.getFunctionType(input_types, output_types));
  func_op.setPrivate();

  return func_op;
}

TF::StatefulPartitionedCallOp EncapsulateOpsInFunc(
    OpBuilder& builder, const llvm::SetVector<Operation*>& ops,