// Test to make sure optional parameters survive a roundtrip

func.func @main(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>) -> tensor<40x40xf32> {
// CHECK: [[NONE:%.*]] = "tfl.no_value"() <{value}> : () -> none
// CHECK: "tfl.fully_connected"(%arg0, %arg1, [[NONE]])
// CHECK-SAME: (tensor<40x37xf32>, tensor<40x37xf32>, none) -> (tensor<40x40xf32>, tensor<40x40xf32>)
  %cst = "tfl.no_value"() {value = unit} : () -> none

func.func @main(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> {
^bb0(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>):
  %cst = arith.constant dense<1.0> : tensor<40xf32>
  %0:2 = "tfl.fully_connected"(%arg0, %arg1, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<40x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> (tensor<40x40xf32>, tensor<40x40xf32>)

  // CHECK: return [[RI]]
  func.return %ri : tensor<!tf_type.resource<tensor<10x3xf32>>>
}

// -----

// CHECK-LABEL:func @partitioned_2d
// CHECK-SAME: ([[ARG0:%.*]]: tensor<10x3xf32>, [[ARG1:%.*]]: tensor<10x3xf32>)
func.func @partitioned_2d(%arg0: tensor<10x3xf32>, %arg1: tensor<10x3xf32>) -> tensor<20x3xf32> {
  // CHECK: [[PI_0:%.*]] = "tf.TPUPartitionedInputV2"([[ARG0]], [[ARG1]])

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

func.func @main(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> {
^bb0(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>):
  // CHECK:      {
  // CHECK-NEXT:  version: 3,
  // CHECK-NEXT:  operator_codes: [ {
  // CHECK-NEXT:    deprecated_builtin_code: 9,
  // CHECK-NEXT:    version: 1,
  // CHECK-NEXT:    builtin_code: FULLY_CONNECTED

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

func.func @main(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> {
^bb0(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>):
  // CHECK:      {
  // CHECK-NEXT:  version: 3,
  // CHECK-NEXT:  operator_codes: [ {
  // CHECK-NEXT:    deprecated_builtin_code: 9,
  // CHECK-NEXT:    version: 2,
  // CHECK-NEXT:    builtin_code: FULLY_CONNECTED

  // FB-NEXT:       } ],

  // CHECK-LABEL: func @main(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>)
  // CHECK-SAME:      -> tensor<40x40xf32>
  // CHECK:         %[[stat:.*]] = "quantfork.stats"(%arg0) <{layerStats = dense
  // CHECK-SAME:      <[-1.000000e+00, 1.000000e+00]> : tensor<2xf32>}>
  // CHECK-SAME:      : (tensor<40x37xf32>) -> tensor<40x37xf32>
  // CHECK-NEXT:    %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<

}

func.func @fully_connected(%arg0: tensor<1x37xf32>, %arg1: tensor<40x37xf32>, %arg2: tensor<40xf32>) -> tensor<1x40xf32> {
  // CHECK: _arithmetic_count = 3000 : i64
  %0 = "tfl.fully_connected"(%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> tensor<1x40xf32>
  func.return %0 : tensor<1x40xf32>
}

  %2 = "tf.Const"() {value = dense<0.0> : tensor<2x2xf32>} : () -> tensor<2x2xf32>
  %3 = "tf.Const"() {value = dense<0.0> : tensor<4x4xf32>} : () -> tensor<4x4xf32>
  "tf.SendTPUEmbeddingGradients"(%2, %3) {_tpu_embedding_layer = "call1", config = "\0A\0B\0C\0D", operandSegmentSizes = array<i32: 2, 0>} : (tensor<2x2xf32>, tensor<4x4xf32>) -> ()

  return %3 : tensor<1x2xf32>
}

// CHECK: func @cast_bf16_matmul_to_fp32
// CHECK-DAG: %[[cst:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<10x2xf32>}> : () -> tensor<10x2xf32>
// CHECK: %[[matmul:.*]] = "tf.MatMul"(%arg0, %[[cst]])
// CHECK: %[[identity:.*]] = "tf.IdentityN"(%[[matmul]])
// CHECK: return %[[identity]] : tensor<1x2xf32>

  // CHECK: %[[RES1:.*]] = "tfl.batch_matmul"(%[[RES0]], %arg2) <{adj_x = false, adj_y = false, asymmetric_quantize_inputs = false}> : (tensor<8x4xf32>, tensor<4x256xf32>) -> tensor<8x256xf32>
  %2 = "tfl.batch_matmul"(%1, %arg2) {adj_x = true, adj_y = false, asymmetric_quantize_inputs = false} : (tensor<4x8xf32>, tensor<4x256xf32>) -> tensor<8x256xf32>
  func.return %2 : tensor<8x256xf32>