func.func @leaves_remote_calls_alone(%arg0: tensor<!tf_type.string>, %arg1: tensor<!tf_type.string>, %arg2: tensor<i64>) {
  // CHECK: RemoteCall
  // CHECK-SAME: (tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<i64>) -> tensor<!tf_type.string>
  %0 = "tf.RemoteCall"(%arg1, %arg0, %arg2) <{f = @__inference__next_func_3760}> {device = ""} : (tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<i64>) -> tensor<!tf_type.string>

                                                                               \
  REGISTER_KERNEL_BUILDER(                                                     \
      Name("RemoteCall").Device(DEVICE).HostMemory("target"), RemoteCallOp);   \
                                                                               \
  REGISTER_KERNEL_BUILDER(                                                     \

    if (!symbol) return failure();
    auto f = llvm::dyn_cast<mlir::func::FuncOp>(symbol);

    if (call.getOperation()->getNumOperands() !=
        f.getBody().front().getNumArguments()) {
      return failure();  // RemoteCall et al
    }

    rewriter.startOpModification(f);
    bool changed = false;
    for (auto [call_arg, body_arg] :
         llvm::zip(call.getOperation()->getOperands(),

    static bool isCompatibleReturnTypes(TypeRange inferred, TypeRange actual) {
      return ArraysAreCastCompatible(inferred, actual);
    }
  }];
}

def TF_RemoteCallOp : TF_Op<"RemoteCall", [CallOpInterface]> {
  let summary = "Runs function `f` on a remote device indicated by `target`.";

  let arguments = (ins