%0 = "tfl.unidirectional_sequence_lstm"(%arg0,...

  // CHECK:  return
}

func.func @tile(tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32> {
^bb0(%arg0: tensor<2x3xf32>, %arg1: tensor<2xi32>):
  %cst = "tf.Const"() { value = dense<[1, 2]> : tensor<2xi32> } : () -> tensor<2xi32>
  %0 = "tf.Tile"(%arg0, %cst) : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32>
  func.return %0 : tensor<2x6xf32>

  // CHECK-LABEL: tile

  %12 = mhlo.clamp %9, %11, %1 : tensor<2xf32>
  %13 = mhlo.multiply %arg0, %12 : tensor<2xf32>
  %14 = mhlo.divide %13, %1 : tensor<2xf32>
  return %14 : tensor<2xf32>
}

// CHECK-LABEL:   func.func @hardswish(
// CHECK-SAME:                         %[[VAL_0:.*]]: tensor<2xf32>) -> tensor<*xf32> {
// CHECK:           %[[VAL_1:.*]] = "tfl.hard_swish"(%[[VAL_0]]) : (tensor<2xf32>) -> tensor<2xf32>

  // Look for resource or variant element type and ensure we refine the subtype.
  // We only support a single subtype at the moment, we won't handle something
  // like:
  //   tensor<!tf_type.variant<tensor<10xf32>, tensor<8xf32>>
  if (rhs_element_type_with_subtype &&
      rhs_element_type_with_subtype.GetSubtypes().size() == 1) {
    auto lhs_element_type_with_subtype =

from tensorflow.compiler.mlir.tensorflow.gen_mlir_passthrough_op import mlir_passthrough_op

mlir_module = '''python
func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {
   %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>
   return %ret : tensor<10x10xf32>
}
'''

@tf.function
def foo(x, y):