// Checks if the `Method` attatched to the given `tf.XlaCallModule` op has
// `WeightOnlyPtq`.
bool HasWeightOnlyPtqMethod(TF::XlaCallModuleOp xla_call_module_op);

// Checks if an op is a `tf.XlaCallModule` op, contains 'conv' or 'dot_general'
// in its name and has `Method` with `WeightOnlyPtq`.
bool IsWeightOnlyQuantizableOp(const Operation& op);

// Lists the functions in a ModuleOp sorted by their names.

  return spec;
}

QuantizationSpec GetDefaultWeightOnlyPtqSpec() {
  QuantizationSpec spec{};
  spec.mutable_matcher()->mutable_function_name()->set_regex(
      "^.*(conv|dot_general).*");

  WeightOnlyPtq& weight_only_ptq_spec =
      *spec.mutable_method()->mutable_weight_only_ptq();
  if (auto [iter, inserted] =
          weight_only_ptq_spec.mutable_input_quantized_types()->try_emplace(1);

    StringRef function_name = entry_function.getValue();
    if (!function_name.starts_with("composite_")) {
      return spec;
    }

    if (function_name.contains("conv")) {
      // Looks up `Method` to see if it should be per-channel quantized and
      // populates the spec accordingly.
      PopulateCoeffOpQuantDimIfPerChannelQuantized(call_op, *spec);

   func @_func(%input: tensor<2x112x112x12xf32>,
              %filter: tensor<7x7x3x64xf32>) {
     %filter_transform = "tf.Pad/tf.Transpose/tf.Reshape"(%filter):
       tensor<7x7x3x64xf32>) -> tensor<4x4x12x64xf32>
     %conv = "tf.Conv2D"(%input, %filter_transform) {strides = [1, 1, 1, 1]}:
       (tensor<2x112x112x12xf32>, tensor<4x4x12x64xf32>) ->
       tensor<2x112x112x64xf32>
   }
}
```

### SpaceToDepth Trigger Condition