// CHECK:           [[VAL_12:%.*]]:4 = "tf.SplitV"([[VAL_7]], [[VAL_10]], [[VAL_11]]) : (tensor<40x8xf32>, tensor<4xi32>, tensor<i32>) -> (tensor<10x8xf32>, tensor<10x8xf32>, tensor<10x8xf32>, tensor<10x8xf32>)
// CHECK-DAG:       [[VAL_13:%.*]] = "tf.Const"() <{value = dense<10> : tensor<4xi32>}> : () -> tensor<4xi32>

  %cst2 = arith.constant dense<2.0> : tensor<1x40xf32>

  %0 = "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>)
  %1 = "tfl.add"(%0, %cst2) {fused_activation_function = "NONE"} : (tensor<40x40xf32>, tensor<1x40xf32>) -> tensor<40x40xf32>

  func.return %1 : tensor<40x40xf32>

// 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>)

  %9 = "mhlo.compare"(%0, %8) {comparison_direction = #mhlo<comparison_direction NE>} : (tensor<10x8xf32>, tensor<10x8xf32>) -> tensor<10x8xi1>
  %10 = mhlo.and %7, %9 : tensor<10x8xi1>
  %11 = mhlo.subtract %arg0, %6 : tensor<10x8xf32>
  %12 = mhlo.divide %11, %5 : tensor<10x8xf32>
  %13 = mhlo.add %12, %3 : tensor<10x8xf32>

  // 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<

// CHECK-LABEL: reduce_window_max_activation_transpose
// CHECK-SAME: (%[[ARG:.+]]: tensor<16x8xf32>) -> tensor<4x8xf32>
func.func @reduce_window_max_activation_transpose_rank2(%arg0: tensor<16x8xf32>) -> tensor<4x8xf32> {
  %0 = stablehlo.constant dense<0xFF800000> : tensor<f32>  // -inf
  %1 = stablehlo.transpose %arg0, dims = [1, 0] : (tensor<16x8xf32>) -> tensor<8x16xf32>
  %2 = "stablehlo.reduce_window"(%1, %0) ({

  %arg9: tensor<2048xf32>,
  %arg10: tensor<2048xf32>,
  %arg11: tensor<2048xf32>,
  %arg12: tensor<2048xf32>,
  %arg13: tensor<640x2048xf32>,
  %arg14: tensor<640xf32>,
  %arg15: tensor<2048xf32>,
  %arg16: tensor<2048xf32>,
  %arg17: tensor<2048xf32>,
  %arg18: tensor<2048xf32>,
  %arg19: tensor<1x640xf32>,
  %arg20: tensor<1x2048xf32>
) -> tensor<1x640xf32> {

  // CHECK: return %[[RESULT]]
  func.return %0: tensor<10x2xf32>
}

// -----

func.func @select_first(%arg0: tensor<10x2xf32>, %arg1: tensor<10x2xf32>) -> tensor<10x2xf32> {
  func.return %arg0: tensor<10x2xf32>
}

// CHECK-LABEL: testMultiInputLegacyCallOp
func.func @testMultiInputLegacyCallOp(%arg0: tensor<10x2xf32>, %arg1: tensor<10x2xf32>) -> tensor<10x2xf32> {

    %0 = "tf.Identity"(%cst) {device = ""} : (tensor<i1>) -> tensor<i1>
    %1 = "tf.PartitionedCall"(%arg0, %cst_1, %cst_0) <{config = "", config_proto = "", executor_type = "", f = @composite_matmul_with_bias_fn_1}> {_tfl_quant_trait = "fully_quantizable"} : (tensor<1x4xf32>, tensor<4x3xf32>, tensor<3xf32>) -> tensor<1x3xf32>