// CHECK-LABEL: func @convert_to_legacy_attribute
func.func @convert_to_legacy_attribute(%arg0: tensor<*xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<*xf32> attributes {tf._disable_call_shape_inference = true, tf.signature.is_stateful} {
  // CHECK-NOT: _replication_info =
  %0 = tf_executor.graph {

                         op->getSuccessors(), new_regions);
      inner_op = rewriter.create(state);
    } else {
      bool disable_call_shape_inference = false;
      if (op->hasAttr("_disable_call_shape_inference")) {
        disable_call_shape_inference =
            op->getAttrOfType<BoolAttr>("_disable_call_shape_inference")
                .getValue();
      }
      inner_op =

             $config, $config_proto, $executor_type),
            (CallOp $f, $args),
          [(ArgTypesMatchCallee $op, $args, $f)]>;
}

// The extra attr on this op is _disable_call_shape_inference, which we ignore
// in the bridge.
def : Pat<(TF_LegacyCallOp:$op $args, FlatSymbolRefAttr:$f, $attr),
          (CallOp $f, $args),
        [(ArgTypesMatchCallee $op, $args, $f)]>;

    mlir::SymbolRefAttr val =
        mlir::SymbolRefAttr::get(builder_.getContext(), node_type_name);
    result.addAttribute("f", val);

    if (!result.attributes.get("_disable_call_shape_inference")) {
      result.addAttribute("_disable_call_shape_inference",
                          builder_.getBoolAttr(false));
    }
  }

  auto composite_control_flow_op = [&](const std::string& name) {

    instead executes it on the device assigned to this op.
  }];

  let arguments = (ins
    Variadic<TF_Tensor>:$args,

    FlatSymbolRefAttr:$f,
    DefaultValuedOptionalAttr<BoolAttr, "false">:$_disable_call_shape_inference
  );

  let results = (outs
    Variadic<TF_Tensor>:$output
  );

  let extraClassDeclaration = [{
    // Gets the argument operands to the called function.

  // CHECK: %[[RESULT:.*]] = call @select_first(%arg0, %arg1) : (tensor<10x2xf32>, tensor<10x2xf32>) -> tensor<10x2xf32>
  %0 = "tf.LegacyCall"(%arg0, %arg1) {_disable_call_shape_inference = true, _tpu_replicate = "cluster", device = "", f = @select_first} : (tensor<10x2xf32>, tensor<10x2xf32>) -> tensor<10x2xf32>
  // CHECK: return %[[RESULT]]
  func.return %0: tensor<10x2xf32>
}