"vhlo.return_v1"(%1112) : (tensor<i1>) -> ()
    }) : (tensor<448xf32>, tensor<448xi32>) -> (tensor<448xf32>, tensor<448xi32>)
  return %0 : tensor<448xf32>
}

//CHECK:func.func private @sort(%arg0: tensor<448xf32>, %arg1: tensor<448xi32>) -> tensor<448xf32> {
//CHECK-NEXT:  %0:2 = "vhlo.sort_v1"(%arg0, %arg1) <{dimension = #vhlo.integer_v1<0 : i64>, is_stable = #vhlo.bool_v1<true>}> ({

  %9 = mhlo.floor %8 : tensor<64xf32>
  %10 = mhlo.convert %9 : (tensor<64xf32>) -> tensor<64xi32>
  %11 = mhlo.compare  LT, %10, %1,  SIGNED : (tensor<64xi32>, tensor<64xi32>) -> tensor<64xi1>
  %12 = mhlo.add %10, %0 : tensor<64xi32>
  %13 = mhlo.select %11, %12, %10 : tensor<64xi1>, tensor<64xi32>
  %14 = mhlo.reshape %13 : (tensor<64xi32>) -> tensor<64x1xi32>

}

// -----

// CHECK-LABEL: func @testBroadcastGradientArgs
func.func @testBroadcastGradientArgs(%s0: tensor<4xi32>, %s1: tensor<4xi32>) -> (tensor<1xi32>, tensor<0xi32>) {
  // CHECK:     tf.BroadcastGradientArgs
  %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) : (tensor<4xi32>, tensor<4xi32>) -> (tensor<1xi32>, tensor<0xi32>)
  func.return %r0, %r1 : tensor<1xi32>, tensor<0xi32>
}

// -----

// CHECK-LABEL: @acos

    >} : (
      tensor<?x?x6xi8>, tensor<6x8xi8>
    ) -> tensor<?x?x8xi32>
  %1 = chlo.broadcast_add %0, %zp_offset : (
      tensor<?x?x8xi32>, tensor<8xi32>) -> tensor<?x?x8xi32>
  %2 = chlo.broadcast_add %1, %bias : (
      tensor<?x?x8xi32>, tensor<8xi32>) -> tensor<?x?x8xi32>
  return %2 : tensor<?x?x8xi32>
}

// -----

// CHECK-LABEL: func @dot_general_add_add_static

// CHECK-NEXT:  %[[argmax:.*]] = "tfl.arg_max"(%[[softmax]], %[[cst]]) : (tensor<128x16xf32>, tensor<i32>) -> tensor<128xi32>
  %3 = "tfl.arg_max"(%2, %cst) : (tensor<128x16xf32>, tensor<i32>) -> tensor<128xi32>
// CHECK-NEXT:  return %[[softmax]], %[[argmax]] : tensor<128x16xf32>, tensor<128xi32>
  func.return %2, %3 : tensor<128x16xf32>, tensor<128xi32>
}

// CHECK-LABEL: PruneUnusedLstm

  %min = arith.constant dense<0.0> : tensor<48xf32>
  %max = arith.constant dense<255.0> : tensor<48xf32>
  %mini = "tf.Identity"(%min) : (tensor<48xf32>) -> tensor<48xf32>
  %maxi = "tf.Identity"(%max) : (tensor<48xf32>) -> tensor<48xf32>
  %s1 = arith.constant dense<[3, 3, 48]> : tensor<3xi32>
  %s2 = arith.constant dense<[3, 3, 48, 1]> : tensor<4xi32>

    %2 = "tf.Const"() {value = dense<[7, 7, 3, 64]> : tensor<4xi32>} : () -> tensor<4xi32>
    %3 = "tf.Const"() {value = dense<[[0, 0], [3, 3], [3, 3], [0, 0]]> : tensor<4x2xi32>} : () -> tensor<4x2xi32>
    %4 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %5 = "tf.Pad"(%arg0, %3) : (tensor<2x224x224x3xf32>, tensor<4x2xi32>) -> tensor<2x230x230x3xf32>
    // CHECK: "tf.Conv2D"

  %min = arith.constant dense<0.0> : tensor<48xf32>
  %max = arith.constant dense<15.0> : tensor<48xf32>
  %mini = "tf.Identity"(%min) : (tensor<48xf32>) -> tensor<48xf32>
  %maxi = "tf.Identity"(%max) : (tensor<48xf32>) -> tensor<48xf32>
  %s1 = arith.constant dense<[3, 3, 48]> : tensor<3xi32>
  %s2 = arith.constant dense<[3, 3, 48, 1]> : tensor<4xi32>

// CHECK:           [[VAL_7:%.*]] = "tfl.slice"([[VAL_1]], [[VAL_3]], [[VAL_5]]) : (tensor<1x8x8x1024xf32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x8x8x256xf32>
// CHECK:           [[VAL_8:%.*]] = "tfl.slice"([[VAL_1]], [[VAL_4]], [[VAL_5]]) : (tensor<1x8x8x1024xf32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x8x8x256xf32>

  %8 = "tfl.mul"(%2, %3) {fused_activation_function = "NONE"} : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>

  func.return %5, %6, %7, %8 : tensor<i32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>
}

// CHECK-LABEL: @add_dense_splat_int
func.func @add_dense_splat_int() -> tensor<4xi32> {
  %0 = arith.constant dense<[-10, -1, 42, 100]> : tensor<4xi32>