f = @identity} : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  %2 = "tf.OptionalGetValue"(%1) : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<f32>
  %3 = "tf.OptionalNone"() : () -> tensor<!tf_type.variant<tensor<f32>>>
  return %2, %3 : tensor<f32>, tensor<!tf_type.variant<tensor<f32>>>
}

func.func private @identity(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {

}

def BatchFunctionOp : TensorflowMlrt_Op<"batch_function", [Pure]> {
  let summary = "Fallback ExecuteOp specialized for tf.BatchFunction.";

  let description = [{
    This kernel executes a variant tf.BatchFunction kernel that supports having
    the `f` attribute as a bytecode function.

    Example:
      %res = tf_mlrt.batch_function(%input, %captured_input)  {
          device = "/device:CPU:0",

        %3 = "tf.OptionalHasValue"(%2) : (tensor<!tf_type.variant>) -> tensor<i1>
        %4 = "tf.IfRegion"(%3) ({
          %5 = "tf.OptionalGetValue"(%2) : (tensor<!tf_type.variant>) -> tensor<32xf32>

  %5 = "tf.Identity"(%4) {__op_key = 10: i32, device = "/device:CPU:0"} : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>
  func.return %5 : tensor<!tf_type.variant>
}
// CHECK-LABEL: __inference_Dataset_map_lambda_160

        "composite" tensors, such as `tf.RaggedTensor`, as inputs.
    *   Fix device placement issues related to datasets with ragged tensors of
        strings (i.e. variant encoded data with types not supported on GPU).
    *   'experimental_follow_type_hints' for tf.function has been deprecated.
        Please use input_signature or reduce_retracing to minimize retracing.

  let summary = [{
Converts the given variant tensor to an iterator and stores it in the given resource.
  }];

  let arguments = (ins
    Arg<TF_ResourceTensor, [{A handle to an iterator resource.}], [TF_DatasetIteratorWrite]>:$resource_handle,
    Arg<TF_VariantTensor, [{A variant tensor storing the state of the iterator contained in the
resource.}]>:$serialized
  );