let arguments = (
    ins TFL_TensorOf<[F32, QI8, QI16]>:$input,

    // Weights
    TFL_TensorOfOrNone<[F32, QI8]>:$input_to_input_weights,
    TFL_TensorOf<[F32, QI8]>:$input_to_forget_weights,
    TFL_TensorOf<[F32, QI8]>:$input_to_cell_weights,
    TFL_TensorOf<[F32, QI8]>:$input_to_output_weights,

    // Recurrent weights
    TFL_TensorOfOrNone<[F32, QI8]>:$recurrent_to_input_weights,

//
// 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:

      continue;
    }

    if (broadcast_args_pivot != parent_broadcast_args) {
      return false;
    }
  }
  return true;
}

// Return true when the given element_type is QI8.
bool IsQI8Type(Type element_type) {
  auto quantized_type = element_type.dyn_cast<QuantizedType>();
  return quantized_type != nullptr &&
         quantized_type.getStorageTypeIntegralWidth() == 8 &&

// CHECK: tfl.transpose
// CHECK: stablehlo.dot_general
// CHECK-NOT: tfl.fully_connected
// CHECK: tfl.quantize

// -----

// Tests static range quantized dot_general with qi32 -> qi8 requantization is
// properly lowered to `tfl.batch_matmul`.

func.func @testMaxPool2DWrongOperandResultType(tensor<1x7x7x16xi32>) -> tensor<1x7x7x16xi32> {
^bb0(%arg0: tensor<1x7x7x16xi32>):
  // expected-error @+1 {{'tfl.max_pool_2d' op operand #0 must be tensor of 32-bit float or QUI8 type or QI8 type or QI16 type or TFLite quint8 type values, but got 'tensor<1x7x7x16xi32>'}}