}

// -----

// CHECK-LABEL: xla_sharding
func.func @xla_sharding(%arg0: tensor<4x16xf32>) -> tensor<4x16xf32> {
  // CHECK-NEXT: mhlo.custom_call @Sharding(%arg0) {mhlo.sharding = ""}
  %0 = "tf.XlaSharding"(%arg0) {_XlaSharding = "", sharding = ""} : (tensor<4x16xf32>) -> tensor<4x16xf32>
  func.return %0 : tensor<4x16xf32>
}

// -----

// CHECK-LABEL: inplace_update_one

// CHECK:           %[[VAL_5:.*]] = "tf.Const"() <{value = dense<[1, 0]> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK:           %[[VAL_6:.*]] = "tf.Transpose"(%[[VAL_4]], %[[VAL_5]]) : (tensor<1x1xi32>, tensor<2xi64>) -> tensor<1x1xi32>
// CHECK:           return %[[VAL_6]] : tensor<1x1xi32>
// CHECK:         }

  func.return %1 : tensor<16xi32>
  // CHECK-DAG: %[[CST:.*]] = arith.constant dense<-1> : tensor<1xi32>
  // CHECK: %[[ARG_MIN:.*]] = "tfl.arg_min"(%arg0, %[[CST]]) : (tensor<16x1024xf32>, tensor<1xi32>) -> tensor<16xi32>
  // CHECK: return %[[ARG_MIN]] : tensor<16xi32>
}

// CHECK-LABEL: DontRemoveSoftmaxNegativeBetaBeforeArgmax
func.func @DontRemoveSoftmaxNegativeBetaBeforeArgmax(%arg0: tensor<16x1024xf32>) -> tensor<16xi32> {

  %19 = "tf.StringLength"(%arg0) {device = "", unit = "BYTE"} : (tensor<1x!tf_type.string>) -> tensor<1xi32>
  %20 = "tf.ExpandDims"(%19, %7) {device = ""} : (tensor<1xi32>, tensor<i32>) -> tensor<1x1xi32>
  %21 = "tf.Cast"(%20) {Truncate = false, device = ""} : (tensor<1x1xi32>) -> tensor<1x1xi64>
  %22 = "tf.Reshape"(%21, %12) {device = ""} : (tensor<1x1xi64>, tensor<1xi64>) -> tensor<1xi64>