%0:2 = tf_executor.graph {
            %outputs, %control = tf_executor.island wraps "tf.Add"(%arg0, %arg1) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
            %outputs_0, %control_1 = tf_executor.island wraps "tf.Add"(%outputs, %arg1) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
            %outputs_2, %control_3 = tf_executor.island wraps "tf.Print"(%outputs_0) {message = "add result"} : (tensor<*xi32>) -> tensor<*xi32>

// `tfl.batch_matmul` when it accepts uniform quantized tensors.
//
// StableHLO Quantizer output:
//   * input: per-tensor qi8
//   * filter: per-channel qi8 for non-batching op, per-tensor for batching op.
//   * output: per-tensor qi32
// JAX Quantizer output:
//   * input: per-tensor qi8
//   * filter: per-channel qi8
//   * output: per-tensor qi8
//
// Conditions for the `tfl.batch_matmul` conversion:

          inputs={'x1': in_placeholder_1}, outputs={'output1': output_tensor_1}
      )

      in_placeholder_2, output_tensor_2 = self._create_simple_tf1_conv_model()
      sig_def_2 = signature_def_utils_impl.predict_signature_def(
          inputs={'x2': in_placeholder_2}, outputs={'output2': output_tensor_2}
      )