func.return %4 : tensor<4x72x72xf32>

// CHECK:      %[[R0:.*]] = "mhlo.concatenate"(%arg0, %arg2, %arg4, %arg6) <{dimension = 0 : i64}> : (tensor<1x72x128xf32>, tensor<1x72x128xf32>, tensor<1x72x128xf32>, tensor<1x72x128xf32>) -> tensor<4x72x128xf32>

func.func @div_dense_different_rank() -> tensor<1x2x2xf32> {
  %cst_0 = arith.constant dense<[[[1.0],[2.0]]]> : tensor<1x2x1xf32>
  %cst_1 = arith.constant dense<[[2.0, 3.0]]> : tensor<1x2xf32>

  %0 = "tfl.div"(%cst_0, %cst_1) {fused_activation_function = "NONE"} : (tensor<1x2x1xf32>, tensor<1x2xf32>) -> tensor<1x2x2xf32>

  func.return %0 : tensor<1x2x2xf32>

func.func @expandDims(%arg0: tensor<2x2xf32>, %arg1: tensor<i32>) -> tensor<1x2x2xf32> {
  %0 = "tf.ExpandDims"(%arg0, %arg1) : (tensor<2x2xf32>, tensor<i32>) -> tensor<1x2x2xf32>
  func.return %0 : tensor<1x2x2xf32>

// CHECK-LABEL:expandDims
// CHECK:  "tfl.expand_dims"(%arg0, %arg1) : (tensor<2x2xf32>, tensor<i32>) -> tensor<1x2x2xf32>
}

func.func @squeezeDefault(%arg0: tensor<1x2x2xf32>) -> tensor<2x2xf32> {

  // CHECK-DAG: %[[ITEMS_0:.*]] = "tf.ExpandDims"(%[[ITEMS]]#0, %[[AXIS]])
  // CHECK-DAG: %[[RESULT:.*]] = "tf.ConcatV2"(%[[ITEMS_1]], %[[ITEMS_0]], %[[AXIS]]) : (tensor<1x2x2xf32>, tensor<1x2x2xf32>, tensor<i64>) -> tensor<2x2x2xf32>
  // CHECK: return %[[RESULT]]

  %indices = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>