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):
  return mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])

// Transforms region bases control flow operations in the TensorFlow dialect to
// their functional counterparts.
std::unique_ptr<OperationPass<ModuleOp>>
CreateTFRegionControlFlowToFunctional();

// Materialize the MlirPassthroughOp by replacing it with the MLIR module
// attached as an attribute.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateMaterializePassthroughOpPass();

      return %2 : tensor<?xi32>
    ```
  }];
}

def MaterializePassthroughOp : Pass<"tf-materialize-passthrough-op", "mlir::func::FuncOp"> {
  let summary = "Materialize the MlirPassthroughOp by replacing it with the "
    "MLIR module attached as an attribute";
  let constructor = "TF::CreateMaterializePassthroughOpPass()";
  let description = [{