// CHECK-LABEL: main
func.func @main() -> tensor<3x2xi32> {
  // CHECK: "tfl.pseudo_const"() <{value = dense<0> : tensor<3x2xi32>}> {tfl.is_variable} : () -> tensor<3x2xi32>
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>

  // CHECK-LABEL: testSingleLstm
  // CHECK-DAG:  %[[CST_0:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf32>}> : () -> tensor<4x4xf32>
  // CHECK-DAG:  %[[CST_1:.*]] = "tfl.pseudo_const"() <{value = dense<0.000000e+00> : tensor<4x4xf32>}> : () -> tensor<4x4xf32>

  // CHECK: %[[CST:.*]] = "tfl.pseudo_const"() <{value = dense<[1, 2, 3, 4]> : tensor<4xi32>}> : () -> tensor<?xi32>
  // CHECK: return %[[CST]]
  %0 = "tfl.reshape"(%input, %shape) : (tensor<2x2xi32>, tensor<1xi32>) -> tensor<?xi32>
  func.return %0 : tensor<?xi32>
}


// CHECK-LABEL: @pseudo_const
func.func @pseudo_const() -> tensor<i32> {

  func.return %0 : tensor<1x512xf32>
}

// CHECK:       func @mean_4d_no_keepdim([[VAL_0:%.*]]: tensor<1x48x48x512xf32>) -> tensor<1x512xf32> {
// CHECK:           [[VAL_1:%.*]] = "tfl.pseudo_const"(){{.*}}dense<[1, 512]> : tensor<2xi32>

func.func @main() -> tensor<2x2xf32> {
  // CHECK: %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
  // 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")

^bb0(%arg0: tensor<4xf32>):
  // CHECK:      [[CONST:%.*]] = "tfl.pseudo_const"() <{value = dense<1.000000e+00> : tensor<4xf32>}> : () -> tensor<4xf32>
  // CHECK-NEXT: [[SQDIFF:%.*]] = tfl.squared_difference %arg0, [[CONST]] : tensor<4xf32>
  // CHECK-NEXT: %{{.*}} = tfl.mul %arg0, [[SQDIFF]] {fused_activation_function = "NONE"} : tensor<4xf32>
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string -

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 ],

module {
func.func @add(%arg0: tensor<3x2xf32>) -> tensor<3x2xf32> attributes {tf.entry_function = {inputs = "serving_default_x", outputs = "outputs"}} {
  %0 = "tfl.pseudo_const" () {value = dense<[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]> : tensor<3x2xf32>} : () -> tensor<3x2xf32>
  %1 = "tfl.add" (%0, %arg0) {fused_activation_function = "NONE"} : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>

func.func @notAnnotateConst(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x30x30x16xf32> {
  // CHECK-NOT: tac.device tac.inference_type
  %0 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<16x3x3x3xf32>} : () -> tensor<16x3x3x3xf32>
  // CHECK-NOT: tac.device tac.inference_type
  %1 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<16xf32>} : () -> tensor<16xf32>
  // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"

  // CHECK-SAME: tfl.schema_version = 3 : i32

  // CHECK:          %{{.*}} = "tfl.pseudo_const"() <{value = dense<{{\[\[1, 2\], \[3, 4\], \[5, 6\]\]}}> : tensor<3x2xi32>}>
  // CHECK-NEXT:     [[SUB:%.*]] = tfl.sub %{{.*}}, %{{.*}} {fused_activation_function = "RELU6"} : tensor<3x2xi32>
  // CHECK-NEXT:     [[SCALAR:%.*]] = "tfl.pseudo_const"() <{value = dense<10> : tensor<i32>}> : () -> tensor<i32>