}

// CHECK-DAG: name: "bar"
func.func @bar() -> tensor<10xf32> {
  %0 = tf_executor.graph {
    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_FLOAT", value = dense<2.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32> loc("const_2")
    tf_executor.fetch %1#0 : tensor<10xf32>
  }
  func.return %0 : tensor<10xf32>

module {

  // One resource, one read. The initial value of the resource is read.
  // CHECK-LABEL: func @add_and_pack(%arg0: tensor<i1>, %arg1: tensor<f32> {tf.resource_name = "x"}) -> tensor<2xf32>
  func.func @add_and_pack(%arg0: tensor<i1>) -> tensor<2xf32> {
    // CHECK-NOT: "tf.VarHandleOp"
    // CHECK-NOT: "tf.ReadVariableOp"
    // CHECK: %[[CONST:.*]] = "tf.Const"()
    // CHECK: %[[ADD:[0-9]*]] = "tf.AddV2"(%arg1, %[[CONST]])

//   start_index_map: 1
// }
func.func @xla_gather(%arg0: tensor<?x2xf32>, %arg1: tensor<1xi32>, %arg2: tensor<2xi32>) -> tensor<*xf32> {
  %0 = "tf.XlaGather"(%arg0, %arg1, %arg2) {device = "", dimension_numbers = "\0A\01\00\12\01\01\1A\01\01", indices_are_sorted = true} : (tensor<?x2xf32>, tensor<1xi32>, tensor<2xi32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

// CHECK: func @xla_gather

  func.return %2 : tensor<2xf32>
}
func.func private @callee(%arg0: tensor<!tf_type.resource<tensor<2xf32>>>) -> tensor<2xf32> {
  %0 = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<2xf32>>>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}


// -----

// Tests main function with multiple blocks.

    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    %cst_0 = arith.constant dense<1.0> : tensor<5x384xf32>
    %cst_1 = arith.constant dense<1.0> : tensor<5x384xf32>
    %0 = "tfl.fully_connected"(%arg0, %cst_0, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<?x384xf32>, tensor<5x384xf32>, tensor<5xf32>) -> tensor<?x5xf32>

  func.func @main() -> (tensor<5xf32> {tf_saved_model.index_path = ["start_logits"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "StatefulPartitionedCall:1"}, tf_saved_model.exported_names = ["serving_default"]} {
    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    func.return %cst : tensor<5xf32>
  }

// CHECK-NEXT:   buffer: 4
// CHECK-NEXT:   } ]
// CHECK-NEXT:   signature_defs: [ ]
// CHECK-NEXT: }

  %0 = "tfl.pseudo_const" () {value = dense<[6]> : tensor<1xi32>} : () -> tensor<1xi32> loc("Const")
  %1 = "tfl.reshape" (%arg0, %0) : (tensor<3x2xi32>, tensor<1xi32>) -> tensor<6xi32>
  func.return %1 : tensor<6xi32>

// CHECK: Placeholder
// CHECK: key: "type"
// CHECK: type: DT_INT8

func.func @main(%arg0 : tensor<16xf32>) {
  tf_executor.graph {
    %1:2 = tf_executor.island wraps "tf.MlirPassthroughOp"(%arg0) {extra_type_attr = [tensor<5xi32>, tensor<16xf32>], Tinputs = [tensor<16xf32>], Toutputs = [tensor<16xf32>], mlir_module = ""} : (tensor<16xf32>) -> tensor<16xf32>
    tf_executor.fetch
  }
  func.return
}

func.func @plain() {
  tf_executor.graph {

}

TEST_F(IsOpFullyQuantizedTest, FalseIfOpNotQuantized) {
  constexpr absl::string_view kNotQuantizedAdd = R"mlir(
    func.func @not_quantized_add(%arg0: tensor<2xf32>) -> tensor<2xf32> {
      %0 = stablehlo.add %arg0, %arg0 : tensor<2xf32>
      return %0 : tensor<2xf32>
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(kNotQuantizedAdd);
  ASSERT_TRUE(module_op);

    quantization_max_val = 127 : i64
  } : (tensor<10xf32>, tensor<f32>, tensor<i32>) -> tensor<10x!tf_type.qint8>
  %1 = "tf.UniformDequantize"(%0, %scale, %zp) {
    quantization_axis = -1 : i64,
    quantization_min_val = -128 : i64,
    quantization_max_val = 127 : i64
  } : (tensor<10x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<10xf32>
  return %1 : tensor<10xf32>
})mlir";