^bb0(%arg0: tensor<8xf32>, %arg3: tensor<f32>, %arg4: tensor<f32>):
  %1 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg3, %arg4) {num_bits = 3, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %1 : tensor<8xf32>

// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK: return %0 : tensor<8xf32>
}

// CHECK:           return %[[VAL_9]] : tensor<384x512xf32>
// CHECK:         }

// -----

func.func @padConcatTo4D(%arg0: tensor<384x384xf32>, %arg1: tensor<384x384xf32>, %arg2: tensor<384x384xf32>, %arg3: tensor<384x384xf32>) -> tensor<1536x384xf32> {

}

func.func @testTanh(%arg0: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 6400 : i64
  %0 = "tfl.tanh"(%arg0) : (tensor<10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>
}

func.func @testAddN(%arg0: tensor<10x10xf32>, %arg1: tensor<10x10xf32>, %arg2: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 200 : i64

  }
// CHECK: func private @mul1(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> attributes {tf.entry_function = {inputs = "mul1_y:0,mul1_x:0", outputs = "PartitionedCall:0"}}
// CHECK:   %[[MUL_0:.*]] = "tf.Mul"(%arg1, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
// CHECK:   return %[[MUL_0]] : tensor<1xf32>
// CHECK: }

// CHECK-LABEL: func @non_contigous_indices
// CHECK-SAME: (%[[ARG_0:.*]]: tensor<i1>, %[[ARG_1:.*]]: tensor<i1>, %[[ARG_2:.*]]: tensor<i1>, %[[ARG_3:.*]]: tensor<i1>, %[[ARG_4:.*]]: tensor<i1>, %[[ARG_5:.*]]: tensor<i1>)
func.func @non_contigous_indices(%arg0: tensor<i1>, %arg1: tensor<i1>, %arg2: tensor<i1>, %arg3: tensor<i1>, %arg4: tensor<i1>, %arg5: tensor<i1>) {
  %0 = "tf.TPUReplicatedInput"(%arg0, %arg0) {index = 8 : i64} : (tensor<i1>, tensor<i1>) -> tensor<i1>

  // CHECK: func @f
  // CHECK: %arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v}
  // CHECK: %arg1: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @w}
  // CHECK: %arg2: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @x}
  // CHECK-NOT: %arg3
  // CHECK-NOT: %arg4
  func.func @f(

  // CHECK-LABEL: concatenate_and_composite
  // CHECK-SAME: %[[ARG0:.*]]: tensor<3x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG2:.*]]: tensor<2x5xf32>
  func.func private @concatenate_and_composite(%arg0: tensor<3x2xf32>, %arg1: tensor<1x2xf32>, %arg2: tensor<2x5xf32>) -> tensor<4x5xf32> {

// CHECK:   }

func.func @num_replicas_replicated(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  %0:2 = tf_executor.graph {
    %control = tf_executor.island() wraps "tf.TPUReplicateMetadata"() {_tpu_replicate = "cluster", device = "/device:TPU:0", num_replicas = 2, topology = "topology"} : () -> ()

    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: list[Value]) -> Operation: ...

class Tf_AddV2Op:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value) -> Operation: ...

class Tf_AnyOp:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value, arg3: bool) -> Operation: ...