func::FuncOp func() {
      return SymbolTable::lookupNearestSymbolFrom<func::FuncOp>(*this, getF());
    }

    // Erases variable arguments from `batch_func_op`. `erase_indices` contains
    // the indices of the arguments to erase.
    void eraseArguments(const BitVector& erase_indices);

    // Gets the argument operands to the called function.
    operand_range getArgOperands() {

	v_0 := v.Args[0]
	// match: (SETB (TESTQ x x))
	// result: (ConstBool [false])
	for {
		if v_0.Op != OpAMD64TESTQ {
			break
		}
		x := v_0.Args[1]
		if x != v_0.Args[0] {
			break
		}
		v.reset(OpConstBool)
		v.AuxInt = boolToAuxInt(false)
		return true
	}
	// match: (SETB (TESTL x x))
	// result: (ConstBool [false])
	for {

		v.AddArg2(v0, v1)
		return true
	}
}
func rewriteValuegeneric_OpNilCheck(v *Value) bool {
	v_1 := v.Args[1]
	v_0 := v.Args[0]
	b := v.Block
	fe := b.Func.fe
	// match: (NilCheck ptr:(GetG mem) mem)
	// result: ptr
	for {
		ptr := v_0
		if ptr.Op != OpGetG {
			break
		}
		mem := ptr.Args[0]
		if mem != v_1 {
			break
		}
		v.copyOf(ptr)

            instead of relying on `Model.inputs` and `Model.input_names`, which
            are no longer set for subclass Models. This attr is set in eager,
            `tf.function`, and graph modes. This gets rid of the need for users
            to manually call `Model._set_inputs` when using Custom Training
            Loops(CTLs).
        *   Dynamic shapes are supported for generators by calling the Model on