// between constant and XlaCallModule op with empty `weight_only_ptq` method
// and function name containing conv.

func.func @qdq_for_conv_weight_empty(%arg0: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32> attributes {tf._original_func_name = "main_0"} {
  %cst = "tf.Const"() {value = dense<3.000000e-01> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
  %0 = "tf.XlaCallModule"(%arg0, %cst) {

    %2 = "tf.XlaCallModule"(%arg0, %1, %0) <{Sout = [#tf_type.shape<?x2>], module = "", version = 9 : i64}> {_entry_function = @composite_dot_general_with_bias_and_relu6_dynamic_fn_2, _original_entry_function = "composite_dot_general_with_bias_and_relu6_dynamic_fn_2", _tfl_quant_trait = "fully_quantizable"} : (tensor<?x2xf32>, tensor<2x2xf32>, tensor<2xf32>) -> tensor<?x2xf32>

  // CHECK: %[[XLA_CALL_MODULE:.*]] = "tf.XlaCallModule"(%[[CUSTOM_AGGREGATOR_1]], %[[SUBGRAPH_1]]) <{Sout = [#tf_type.shape<1024x3>], {{.*}}}> {_entry_function = @composite_dot_general_fn_1, _original_entry_function = "composite_dot_general_fn_1"
  // CHECK: %[[CUSTOM_AGGREGATOR_2:.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%[[XLA_CALL_MODULE:.*]])

    }
  }
)mlir";

// Quantizable ops: XlaCallModule op with "fully_quantizable" attribute and
// same-scale StableHLO ops
// Non-quantizable ops: quantize/dequantize ops
constexpr absl::string_view kModuleCompositeSameScale = R"mlir(
  module {
    func.func @same_scale_after_composite() -> tensor<3x1xf32> {

  constexpr absl::string_view kXlaCallModuleOpWithQuantizationMethodAttr =
      R"mlir(
    func.func @main(%arg0: tensor<1x1x3xf32>, %arg1: tensor<3x4xf32>) -> tensor<1x1x4xf32> {

        if (called_index < 0 || called_index >= function_list.size()) {
          op->emitOpError()
              << "references function #" << called_index
              << " but enclosing XlaCallModule has a function list of size "
              << function_list.size();
          return WalkResult::interrupt();
        }

        llvm::SmallVector<NamedAttribute> new_config;

  ) -> str:
    """Extracts the first XlaCallModule op from output saved model to string."""
    root = load.load(output_saved_model_path)
    tf_graph_def = root.signatures['serving_default'].graph.as_graph_def()
    for function in tf_graph_def.library.function:
      for node_def in function.node_def:
        if node_def.op == 'XlaCallModule':
          with ir.Context() as context:

    %3 = "quantfork.dcast"(%2) : (tensor<2x3x!quant.uniform<i8<-127:127>:f32:1, {6.000000e-03,6.000000e-03,6.000000e-03}>>) -> tensor<2x3xf32>

    %7 = "quantfork.dcast"(%6) : (tensor<2x3x1x1024x!quant.uniform<i8:f32, 5.000000e-01:2>>) -> tensor<2x3x1x1024xf32>

      return failure();
    }
    return success(
        op->hasOneUse() &&
        IsWeightQuantizableFunction(*op->getUses().begin(), type.getRank()));
  }

  // Checks if the operand is second operand of `tf.XlaCallModule` op for
  // `stablehlo.convolution` or `stablehlo.dot_general` with fully_quantizable
  // trait.
  static bool IsWeightQuantizableFunction(OpOperand& operand, int64_t rank) {
    if (operand.getOperandNumber() != 1) {