(And32  (Const32 [c])  (Const32 [d]))  => (Const32 [c&d])
(And64  (Const64 [c])  (Const64 [d]))  => (Const64 [c&d])

(Or8   (Const8 [c])   (Const8 [d]))   => (Const8  [c|d])
(Or16  (Const16 [c])  (Const16 [d]))  => (Const16 [c|d])
(Or32  (Const32 [c])  (Const32 [d]))  => (Const32 [c|d])
(Or64  (Const64 [c])  (Const64 [d]))  => (Const64 [c|d])

(Xor8   (Const8 [c])   (Const8 [d]))   => (Const8  [c^d])

	VPMSUMD	V1,const1,V1
	VAND	V1,mask_32bit,V1
	VPMSUMD	V1,const2,V1
	VXOR	V0,V1,V0
	VSLDOI  $4,V0,zeroes,V0

#endif

	MFVSRD	VS32,R3 // VS32 = V0

	NOR	R3,R3,R3 // return ^crc
	MOVW	R3,ret+32(FP)
	RET

first_warm_up_done:

	LVX	(R3),const1
	ADD	$16,R3

	VPMSUMD	V16,const1,V8
	VPMSUMD	V17,const1,V9
	VPMSUMD	V18,const1,V10
	VPMSUMD	V19,const1,V11
	VPMSUMD	V20,const1,V12

			v2.AuxInt = int64ToAuxInt(d - c)
			v.AddArg2(v0, v2)
			return true
		}
		break
	}
	// match: (AndB (Leq32 (Const32 [c]) x) (Less32 x (Const32 [d])))
	// cond: d >= c
	// result: (Less32U (Sub32 <x.Type> x (Const32 <x.Type> [c])) (Const32 <x.Type> [d-c]))
	for {
		for _i0 := 0; _i0 <= 1; _i0, v_0, v_1 = _i0+1, v_1, v_0 {
			if v_0.Op != OpLeq32 {
				continue
			}

    TF_Operation* const3;
    if (grad_inputs_provided) {
      const float const3_val[] = {1.0, 1.0, 1.0, 1.0};
      const3 = FloatConst2x2(expected_graph_, s_, const3_val, "GradInputs");
    } else {
      const3 = OnesLike(expected_graph_, s_, matmul, "gradients/OnesLike");
    }

    TF_Operation* matmul1 = MatMul(expected_graph_, s_, const3, const1,

Christian Sigg <******@****.***> 1714640622 -0700

  %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32> loc("Const")
  %1 = "tfl.sub" (%arg0, %0) {fused_activation_function = "RELU6"} : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32> loc("sub")
  %2 = "arith.constant" () {value = dense<10> : tensor<i32>} : () -> tensor<i32> loc("Const2")

  func.return %2 : tensor<1x3x4x2xf32>
// CHECK-DAG: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<1x1x3x2xf32>}> : () -> tensor<1x1x3x2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = "tf.Const"() <{value = dense<-1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[CONST_2:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>

// See the License for the specific language governing permissions and
// limitations under the License.

package kclient_test

import (
	"testing"

	"go.uber.org/atomic"
	"sigs.k8s.io/gateway-api/pkg/consts"

	"istio.io/istio/pkg/config/schema/gvr"
	"istio.io/istio/pkg/kube"
	"istio.io/istio/pkg/kube/kclient/clienttest"
	"istio.io/istio/pkg/test"
	"istio.io/istio/pkg/test/util/assert"
)

  func.func private @quantize_dot_general_with_bias_same_shape_fn(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {
    %cst = "tf.Const"() {value = dense<3.00000000e-1> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
    %cst_0 = "tf.Const"() {value = dense<4.00000000e-1> : tensor<1x3xf32>} : () -> tensor<1x3xf32>

  // CHECK-DAG: %[[CONST1:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}>
  // CHECK-DAG: %[[CONST2:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}>
  // CHECK: %[[TRANS1:.*]] = "tf.Transpose"(%arg0, %[[CONST1]]) : (tensor<1x4x4x8xf32>, tensor<4xi32>) -> tensor<1x8x4x4xf32>