%1 = "tf.BroadcastTo"(%arg1, %cst) : (tensor<5x1xf32>, tensor<2xi64>) -> tensor<5x7xf32>
  %2 = "tf.Add"(%0, %1) : (tensor<5x7xf32>, tensor<5x7xf32>) -> tensor<5x7xf32>
  func.return %2 : tensor<5x7xf32>
  // CHECK: %[[V0:.*]] = "tf.Add"(%arg0, %arg1) : (tensor<7xf32>, tensor<5x1xf32>) -> tensor<5x7xf32>
  // CHECK: %[[V0]] : tensor<5x7xf32>
}

// CHECK-LABEL: @broadcast_batch_matmul_v2_rhs

# CHECK-NEXT: %[[READ_VAR_0:.*]] = "tf.ReadVariableOp"(%[[ARG_2]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
# CHECK-NEXT: %[[MATMUL_0:.*]] = "tf.MatMul"(%[[ARG_1]], %[[READ_VAR_0]]) <{{{.*}}}> {{{.*}}} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
# CHECK-NEXT: return %[[MATMUL_0]] : tensor<3x3xf32>


def Test():
  x = tf.constant([[1.0], [1.0], [1.0]])
  y = tf.compat.v1.get_variable(
      name='y',

  # Verify that the function defined using function.Defun
  # has a corresponding tf.LegacyCall op.
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME: [[ARG0:%.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["y"]},
  # CHECK-SAME: [[ARG1:%.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["x"]}
  #
  # CHECK-NEXT: [[R0:%.*]] = "tf.LegacyCall"([[ARG1]], [[ARG0]])
  z = plus(x, y)

func.func @fakeQuantPerChannelForActivation(%arg0: tensor<8x3xf32>) -> (tensor<8x3xf32>) {
  %arg1 = arith.constant dense<[0.0, -1.0, 1.0]> : tensor<3xf32>
  %arg2 = arith.constant dense<[255.0, 254.0, 256.0]> : tensor<3xf32>
  %0 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<8x3xf32>, tensor<3xf32>, tensor<3xf32>) -> tensor<8x3xf32>
  func.return %0 : tensor<8x3xf32>

func.func @fakeQuantPerChannelForActivation(%arg0: tensor<8x3xf32>) -> (tensor<8x3xf32>) {
  %arg1 = arith.constant dense<[0.0, -1.0, 1.0]> : tensor<3xf32>
  %arg2 = arith.constant dense<[15.0, 14.0, 16.0]> : tensor<3xf32>
  %0 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<8x3xf32>, tensor<3xf32>, tensor<3xf32>) -> tensor<8x3xf32>
  func.return %0 : tensor<8x3xf32>

# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME:   [[ARG0:%.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["x"]},
# CHECK-SAME:   [[ARG1:%.*]]: tensor<!tf_type.resource<tensor<1x3xf32>>> {tf_saved_model.bound_input = @[[VAR]]})
# CHECK-SAME:             -> (tensor<3x3xf32> {tf_saved_model.index_path = ["r"]})

func.func @tfl_wrapped_jax_random_normal(%arg0: tensor<2xui32>) -> tuple<tensor<3x4xf32>> {
  // This is a fake jax random normal body.
  %0 = stablehlo.constant dense<0.0> : tensor<12xf32>
  %1 = "stablehlo.reshape"(%0) : (tensor<12xf32>) -> tensor<3x4xf32>
  %2 = "stablehlo.tuple"(%1) : (tensor<3x4xf32>) -> tuple<tensor<3x4xf32>>
  func.return %2 : tuple<tensor<3x4xf32>>
}


// CHECK-LABEL:   func @tfl_wrapped_jax_random_uniform(

func.func @TfBatchMatMulV2(%arg0: tensor<4x128x2xf32>, %arg1:  tensor<2x1xf32>) -> tensor<4x128x1xf32> {
  %0 = "tfl.custom"(%arg0, %arg1) {
    custom_code = "FlexBatchMatMulV2",
    custom_option = #tfl<const_bytes : "0x0D42617463684D61744D756C56320038120D42617463684D61744D756C56321A001A002A070A0154120230012A0B0A0561646A5F78120228002A0B0A0561646A5F791202280032000002493B1414042801">
  } : (tensor<4x128x2xf32>, tensor<2x1xf32>) -> tensor<4x128x1xf32>

# CHECK-NEXT: [[R0:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
# CHECK-NEXT: [[R1:%.*]] = "tf.MatMul"([[ARG0]], [[R0]]) <{{{.*}}}> {{{.*}}} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
# CHECK-NEXT: return [[R1]] : tensor<3x3xf32>


def Test():

  x = tf.constant([[1.0], [1.0], [1.0]])
  y = tf.compat.v1.get_variable(
      name='y',

  %2 = "tfl.add"(%arg0, %arg3) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %3 = "tfl.pack"(%1, %2) {axis = 0 : i32, per_device_costs = {CPU = 2.0 : f32, GPU = -1.0 : f32}, values_count = 2 : i32, tac.device = "CPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>