// `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:

    its predecessor:

    ```mlir
    %outputs1:2, %control1 = tf_executor.island wraps "tf.IteratorGetNext"(%arg)
      {_class = ["loc:@dataset_iterator_1"]}
    %outputs2:2, %control2 = tf_executor.island wraps "tf.Split"(%outputs0, %outputs1#1)
      {_class = ["loc:@dataset_iterator_1", num_split = 2 : i32}
    ```
  }];
}

          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}
      )