func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> {
  %cst = "tfl.pseudo_const"() {value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<?xi32>
  %0 = "tfl.add"(%arg0, %cst) {fused_activation_function = "NONE"} : (tensor<2xi32>, tensor<?xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}


// CHECK:    tensors: [ {
// CHECK-NEXT:      shape: [ 2 ],

func.func @main(%arg0 : tensor<!tf_type.variant<tensor<2xi32>>>, %arg1: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>> {
  func.return %arg0 : tensor<!tf_type.variant<tensor<2xi32>>>
}

// CHECK:          func.func @main(%[[ARG0:.*]]: tensor<!tf_type.variant<tensor<2xi32>>>, %[[ARG1:.*]]: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>>

    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %2:2 = tf_executor.island wraps "tf.PlaceholderWithDefault"(%1#0) {type = i32} : (tensor<i32>) -> tensor<*xi32> loc("unranked")

  // CHECK: %{{.*}} = "tfl.reshape"(%{{.*}}, %[[cst]]) : (tensor<4xf32>, tensor<2xi32>) -> tensor<2x2xf32>
  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.reshape" (%0, %cst) : (tensor<4xf32>, tensor<2xi32>) -> tensor<2x2xf32> loc("reshape")

func.func @main(%arg0: tensor<4xi8>, %arg1: tensor<4xi32>) -> (tensor<4xi32>, tensor<4xi8>)
// CHECK: attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}}
attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}} {
  %0 = "tfl.neg"(%arg0) : (tensor<4xi8>) -> tensor<4xi8> loc("neg")
  %1 = "tfl.neg"(%arg1) : (tensor<4xi32>) -> tensor<4xi32> loc("neg")

    "tfl.yield"(%1#0, %1#1) : (tensor<*xi32>, tensor<*xf32>) -> ()
  }) {is_stateless = false} : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>)
  func.return %0#1 : tensor<1xf32>
}

func.func @cond(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> tensor<i1> {
  %cst = arith.constant dense<0> : tensor<i32> loc("Const")
  %0 = "tfl.greater"(%arg0, %cst) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

    %a, %control1 = tf_executor.island wraps "tf.A"() {_class = ["loc:@class"]} : () -> (tensor<2xf32>)
    %s:2, %control2 = tf_executor.island wraps "tf.Split"(%c, %a) {num_split = 2 : i32} : (tensor<i32>, tensor<2xf32>) -> (tensor<1xf32>, tensor<1xf32>)
    tf_executor.fetch
  }
  func.return
}

// -----

// CHECK-LABEL: func @colocate_split_with_pred_results
func.func @colocate_split_with_pred_results() {
  // CHECK: Split

      } : (tensor<!tf_type.resource<tensor<2xi32>>>) -> tensor<*xi32>
    return %1 : tensor<*xi32>
  }

  func.func private @called_by_batch_func_assign(%arg: tensor<*x!tf_type.resource>) -> tensor<?xi32> {
    %0 = "tf.Const"() {value = dense<4> : tensor<2xi32>} : () -> tensor<2xi32>
    "tf.AssignVariableOp"(%arg, %0) : (tensor<*x!tf_type.resource>, tensor<2xi32>) -> ()

module attributes {tf_saved_model.semantics} {
  // CHECK: ml_program.global private mutable [[V:@.+]](dense<1.000000e+00> : tensor<1xf32>) : tensor<?xf32>
  // CHECK: func.func @f
  // CHECK: [[T:%.+]] = ml_program.global_load [[V]]
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<?xf32>, value = dense<1.> : tensor<1xf32> } : () -> ()
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<?xf32>>> {tf_saved_model.bound_input = @v})

// CHECK-LABEL: func @strip_simple(
// CHECK-NOT: tf._noinline
func.func @strip_simple() -> tensor<2xi32> attributes {tf._noinline = true} {
  // CHECK-NEXT: %[[CST:.*]] = "tf.Const"
  %cst = "tf.Const"() { value = dense<2> : tensor<2xi32> } : () -> tensor<2xi32>
  func.return %cst : tensor<2xi32>