// CHECK:  return
}

func.func @addV2(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> {
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  func.return %0 : tensor<1xi32>

// CHECK-LABEL: addV2
// CHECK:  tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xi32>
}

func.func @addV2I16(%arg0: tensor<1xi16>, %arg1: tensor<1xi16>) -> tensor<1xi16> {

// -----

func.func @testFusedActivationFunction(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>) -> (tensor<4xi32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) {
  // CHECK: "NONE"
  %0 = tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<4xi32>
  // CHECK: "RELU"
  %1 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU"} : tensor<4xi32>
  // CHECK: "RELU_N1_TO_1"

    %cst = "tf.Const"() <{value = dense<[1]> : tensor<1xi32>}> : () -> tensor<1xi32>
    %0 = "tf.VarHandleOp"() <{container = "", shared_name = "Variable"}> : () -> tensor<!tf_type.resource<tensor<2x3xbf16>>>
    %1 = "tf.ResourceGather"(%0, %cst) <{batch_dims = 0 : i64, validate_indices = true}> : (tensor<!tf_type.resource<tensor<2x3xbf16>>>, tensor<1xi32>) -> tensor<1x3xbf16>
    return %1: tensor<1x3xbf16>
  }

    ```mlir
      %2 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>
      %3 = "tf.B"(%2) {device = "tpu0"} : (tensor<?xi32>) -> tensor<?xi32>
      %4 = "tf.C"(%2, %3) {device = "tpu0"} : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
      %5 = "tf.D"(%4) : (tensor<?xi32>) -> tensor<?xi32>
    ```

    After the pass, we will have:

    ```mlir
      %0 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>

  // The first Operand is assumed to be a TensorType around a variant with a
  // single subtype (e.g. tensor<!tf_type.variant<tensor<2xi32>>>). We will
  // copy this type to the first result, and copy the singular variant subtype
  // to the second result (tensor<2xi32>).
  DCOMMENT_OP(op, "Inferring shape for TensorListPopBackOp.");

  auto src_list_handle_t =

from tensorflow.compiler.mlir.tensorflow.gen_mlir_passthrough_op import mlir_passthrough_op

mlir_module = '''python
func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {
   %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>
   return %ret : tensor<10x10xf32>
}
'''

@tf.function
def foo(x, y):

		if z.Op != OpPPC64MOVHZreg {
			break
		}
		x := z.Args[0]
		if !(z.Uses == 1 && c < 16) {
			break
		}
		v.reset(OpPPC64CLRLSLWI)
		v.AuxInt = int32ToAuxInt(newPPC64ShiftAuxInt(c, 16, 31, 32))
		v.AddArg(x)
		return true
	}
	// match: (SLWconst [c] z:(ANDconst [d] x))
	// cond: z.Uses == 1 && isPPC64ValidShiftMask(d) && c<=(32-getPPC64ShiftMaskLength(d))