# CHECK: mlir_module = "\0Afunc @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {\0A %add = \22tf.Add\22(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>\0A %ret = \22magic.op\22(%add, %add) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>\0A return %ret : tensor<10x10xf32>\0A}\0A"}> {device = ""} : (tensor<10xf32>, tensor<10xf32>) -> tensor<*xf32>

node {
  name: "x"
  op: "Placeholder"
  attr {

// CHECK:           }) : (tensor<1x?xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, none, none, none, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<3x1xf32>, tensor<3xf32>, tensor<1x3xf32>, tensor<1x1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x3xf32>

// CHECK-LABEL: func @main
func.func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {
// CHECK-SAME: (%[[ARG0:.*]]: tensor<10xf32>, %[[ARG1:.*]]: tensor<10xf32>)
// CHECK-NEXT:    %[[ADD:.*]] = "tf.Add"(%[[ARG0]], %[[ARG1]]) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>
// CHECK-NEXT:    %[[MAGIC:.*]] = "magic.op"(%[[ADD]], %[[ADD]]) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>
// CHECK-NEXT:    return %[[MAGIC]]

    %3 = "tf.Mul"(%1, %2) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>
    return %3 : tensor<10xf32>
  }
}

// -----

// CHECK-LABEL: module
module attributes {tf_saved_model.semantics} {
  // CHECK: ml_program.global{{.*}}mutable{{.*}}@vars.v
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<10xf32>, value = dense<[0.,10.,2.,3.,4.,5.,6.,7.,8.,9.]> : tensor<10xf32> } : () -> ()

module {
  // CHECK-LABEL: func @main
  // CHECK-SAME: %[[ARG_0:.*]]: tensor<10xf32>, %[[ARG_1:.*]]: tensor<10xf32>, %[[ARG_2:.*]]: tensor<6xi32>
  func.func @main(
      %arg0: tensor<10xf32>, %arg1: tensor<10xf32>, %arg2: tensor<6xi32>
    ) -> (tensor<10xf32>, tensor<6xi32>) {
    // CHECK: %[[CAST_0:.*]] = "tf.Cast"(%[[ARG_0]]) <{Truncate = false}> : (tensor<10xf32>) -> tensor<10xbf16>

  attributes {tf_saved_model.exported_names = ["assign_from_global"]} {
    // CHECK: [[name:%.*]] = "tf.VarHandleOp"
    // CHECK: "tf.AssignVariableOp"([[name]]
    %0 = "tf.Const"() {type = tensor<10xf32>, value = dense<[0.,10.,2.,3.,4.,5.,6.,7.,8.,9.]> : tensor<10xf32>} : () -> tensor<10xf32>
    "tf.AssignVariableOp"(%arg0, %0) : (tensor<!tf_type.resource<tensor<10xf32>>>, tensor<10xf32>) -> ()

  // CHECK-NEXT: %[[SLICE:.*]] = "tf.Slice"(%[[COPY]], %[[SUB]], %[[SLICE_SIZE]]) : (tensor<10xf32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1xf32>
  // CHECK-NEXT: %[[ELEM_SHAPE:.*]] = "tf.Const"() <{value = dense<> : tensor<0xi32>}> : () -> tensor<0xi32>
  // CHECK-NEXT: %[[ELEM:.*]] = "tf.Reshape"(%[[SLICE]], %[[ELEM_SHAPE]]) : (tensor<1xf32>, tensor<0xi32>) -> tensor<f32>

  func.return %0 : tensor<256x32x32x16xf32>
}

func.func @testConv2DDynamicShape(tensor<?x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<?x32x32x16xf32> {
^bb0(%arg0: tensor<?x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>):
  // CHECK: _arithmetic_count = -1 : i64

  %2 = "tf.TensorListStack"(%0, %arg1) : (tensor<!tf_type.variant<tensor<10xf32>>>, tensor<1xi32>) -> tensor<3x10xf32>
  func.return %1, %2 : tensor<10xf32>, tensor<3x10xf32>

// CHECK:  %0 = "tf.Gather"(%arg0, %arg2) <{validate_indices = true}> : (tensor<3x10xf32>, tensor<i32>) -> tensor<10xf32>
// CHECK: return %0, %arg0 : tensor<10xf32>, tensor<3x10xf32>
}

// -----

  func.return %1 : tensor<10x10x10xf32>
}

// -----

func.func @pack_CPU(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<2x100xf32> attributes {tac.device = "CPU", tac.interface_name = "func_2"} {
  // CHECK: tac.cost = 1.000000e+02
  %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, tac.device = "CPU", values_count = 2 : i32} : (tensor<100xf32>, tensor<100xf32>) -> tensor<2x100xf32>