}

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",

}

// -----

// Test lowering BatchFunction op.

func.func @batched_function(%x: tensor<?xi32>) -> tensor<?xi32> {
  return %x : tensor<?xi32>
}

// CHECK-LABEL: func @main
func.func @main(%x: tensor<1xi32>) -> (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) {
  // CHECK: [[y_future:%.*]] = tf_mlrt.batch_function
  // CHECK-SAME: f = @batched_function
  // CHECK-SAME: \22batch_function\22

}

def BatchFunctionOp : FallbackAsync_Op<"batch_function",
    [Pure, CoreRT_TypedAttributeTrait]> {
  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 BEF function.

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

  CopyDeviceAndUnderscoredAttributes(case_op, case_region);

  for (const auto& item : llvm::enumerate(case_region.getBranches())) {
    CreateCall(case_op, case_op.branch_function(item.index()),
               /*caller_region=*/item.value(), case_op.getInput(),
               /*use_region_args=*/false,
               /*forward_block_args=*/false);
  }