// CHECK-DAG:       [[VAL_20:%.*]] = "tf.Const"() <{value = dense<-1> : tensor<1xi32>}> : () -> tensor<1xi32>
// CHECK:           [[VAL_21:%.*]] = "tf.Reshape"([[VAL_15]]#0, [[VAL_20]]) : (tensor<1x10xf32>, tensor<1xi32>) -> tensor<10xf32>

  %0 = "tfl.fully_connected"(%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<2x2x10xf32>, tensor<40x40xf32>, none) -> tensor<1x40xf32>
  func.return %0 : tensor<1x40xf32>
}

// -----

func.func @testFullyConnectedWith3DFilter(%arg0: tensor<1x37xf32>, %arg1: tensor<40x2x37xf32>, %arg2: tensor<40xf32>) -> tensor<1x40xf32> {

func.func @ToBool_2DTensor(%arg0: tensor<1x5xf32>) -> tensor<i1> {
  // CHECK: [[Const:%.*]] = "tf.Const"() <{value = dense<true> : tensor<i1>}> : () -> tensor<i1>
  // CHECK: return [[Const]]
  %0 = "tf.ToBool"(%arg0) : (tensor<1x5xf32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

// CHECK-LABEL: func @ToBool_2DTensorZeroDim
func.func @ToBool_2DTensorZeroDim(%arg0: tensor<1x0xf32>) -> tensor<i1> {

  %0 = "tf.Squeeze"(%arg0) {squeeze_dims = [0]} : (tensor<1x1x10xf32>) -> tensor<1x10xf32>
  %1 = "tf.Squeeze"(%arg1) : (tensor<?x10xf32>) -> tensor<*xf32>
  %2 = "tf.Const"() { value = dense<[2, 5]> : tensor<2xi32> } : () -> tensor<2xi32>
  %3 = "tf.Reshape" (%0, %2) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<2x5xf32>
  %4 = "tf.some_op"(%1, %3) : (tensor<*xf32>, tensor<2x5xf32>) -> i32
  func.return %4 : i32

  func.return %0 : tensor<5x2x5x10xf32>
}

// -----

func.func @testBatchMatMulV2DynamicOutputBatchDimension(%lhs: tensor<1x2x5x10xf32>, %rhs: tensor<1x10xf32>) -> tensor<?x2x5x10xf32> {
  %0 = "tf.BatchMatMulV2"(%lhs, %rhs) : (tensor<1x2x5x10xf32>, tensor<1x10xf32>) -> tensor<?x2x5x10xf32>
  func.return %0 : tensor<?x2x5x10xf32>
}

// -----