},
    {
      // loop body
      ^bb0(%barg0 : tensor<*xf32>, %barg1 : tensor<*xf32>, %barg2 : tensor<*xf32>, %barg3 : tensor<i32>):
      %one = arith.constant dense<1> : tensor<i32>
      %sub = "tf.Sub"(%barg3, %one) : (tensor<i32>, tensor<i32>) -> tensor<i32>
      "tf.Yield"(%barg0, %barg1, %barg2, %sub) : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>, tensor<i32>) -> ()
    }

      "tf.Yield"(%cond, %carg0, %carg1) : (tensor<i1>, tensor<*xf32>, tensor<i32>) -> ()
  }, {
    ^bb0(%barg0: tensor<*xf32>, %barg1: tensor<i32>):
      %add0 = "tf.Add"(%barg0, %barg0) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
      %add1 = "tf.Add"(%barg1, %barg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
      "tf.Yield"(%add0, %add1) : (tensor<*xf32>, tensor<i32>) -> ()

      ^bb0(%barg0: tensor<i32>, %barg1: tensor<!tf_type.variant<tensor<?x1xf32>>>): // no predeceessors
        %cond = "tf.Less"(%barg0, %size) : (tensor<i32>, tensor<i32>) -> tensor<i1>
        "tf.Yield"(%cond) : (tensor<i1>) -> ()
    }, {
      ^bb0(%barg0: tensor<i32>, %barg1: tensor<!tf_type.variant<tensor<?x1xf32>>>): // no predeceessors
      %index = "tf.AddV2"(%barg0, %one) : (tensor<i32>, tensor<i32>) -> tensor<i32>

	v_0 := v.Args[0]
	// match: (Addr {sym} base)
	// result: (MOVVaddr {sym} base)
	for {
		sym := auxToSym(v.Aux)
		base := v_0
		v.reset(OpLOONG64MOVVaddr)
		v.Aux = symToAux(sym)
		v.AddArg(base)
		return true
	}
}
func rewriteValueLOONG64_OpAtomicCompareAndSwap32(v *Value) bool {
	v_3 := v.Args[3]
	v_2 := v.Args[2]
	v_1 := v.Args[1]
	v_0 := v.Args[0]
	b := v.Block

	return t.goString(0, "")
}

func (t *Template) goString(indent int, field string) string {
	var args string
	if len(t.Args) == 0 {
		args = fmt.Sprintf("%*sArgs: nil", indent+2, "")
	} else {
		args = fmt.Sprintf("%*sArgs:", indent+2, "")
		for i, a := range t.Args {
			args += "\n"
			args += a.goString(indent+4, fmt.Sprintf("%d: ", i))
		}
	}

            // expected-remark@above {{ID: 7}}
        },
        {
          ^bb0(%pred: tensor<i1>,
               %barg0: tensor<*x!tf_type.resource<tensor<32xf32>>>,
               %barg1: tensor<*x!tf_type.resource<tensor<32xf32>>>,
               %barg2: tensor<*x!tf_type.resource<tensor<32xf32>>>):
             %graph:4 = tf_executor.graph {
            // expected-remark@above {{ID: 13}}

// [Stmt.ExecContext].
func (s *Stmt) Exec(args ...any) (Result, error) {
	return s.ExecContext(context.Background(), args...)
}

func resultFromStatement(ctx context.Context, ci driver.Conn, ds *driverStmt, args ...any) (Result, error) {
	ds.Lock()
	defer ds.Unlock()

	dargs, err := driverArgsConnLocked(ci, ds, args)
	if err != nil {
		return nil, err
	}

                // GENERATE getAsDynamicObject().invokeMethod(name, (args instanceof Object[]) ? args : new Object[] { args })
                _ALOAD(0);
                _INVOKEVIRTUAL(generatedType, "getAsDynamicObject", RETURN_DYNAMIC_OBJECT);

                _ALOAD(1);

                // GENERATE (args instanceof Object[]) ? args : new Object[] { args }
                Label end = new Label();

    the same TensorList at multiple places and assign different
    TensorListSetItem to elements of TensorList, the shape inference is then
    unable to identify the Shape of these args and thus the input TensorList
    shape is unidentifiable.
    All of these args are supposed to be independent and not related to original
    creation of TensorList.

    This pass will create multiple instances of TensorList for each arg of the

s._toggle(n,!1);e._wrapper=null,Se(n)}})}})}}},ln={mixins:[hn,un],args:"animation",props:{close:String},data:{animation:[!0],selClose:".uk-alert-close",duration:150,hideProps:G({opacity:0},un.data.hideProps)},events:[{name:"click",delegate:function(){return this.selClose},handler:function(t){t.preventDefault(),this.close()}}],methods:{close:function(){var t=this;this.toggleElement(this.$el).then(function(){return t.$destroy(!0)})}}},dn={args:"autoplay",props:{automute:Boolean,autoplay:Boolean},data:{a...