for (auto current_type : result_types) {
    if (mlir::dyn_cast<TensorType>(current_type).getElementType().isF32())
      return true;
  }
  return false;
}

// Unwraps quantization parameters of PartitionedCall ops with quantized
// input/outputs that are created from QuantizePass.
class QuantizeFunctionPattern
    : public mlir::OpRewritePattern<TF::PartitionedCallOp> {
 public:

func.func @serving_default(%arg0: tensor<?xf32> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}) -> tensor<3xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input:0", outputs = "PartitionedCall:0"}} {
  %cst = "tf.Const"() {device = "/job:localhost/replica:0/task:0/device:CPU:0", value = dense<0> : tensor<i32>} : () -> tensor<i32>

    The LegacyCall operation represents a direct call to a function that is
    within the same symbol scope as the call and is mapped to a GraphDef node
    with the function name as the op name. Unlike a PartitionedCall which
    represents asynchronously executing a function across multiple devices, a
    LegacyCall ignores specification for ops in the attached function and
    instead executes it on the device assigned to this op.