return nil, nil, nil, fmt.Errorf("failed to convert extension config struct %+v to Wasm Network filter", typedStruct)
			}
		} else if typedStruct.TypeUrl == model.WasmNetworkFilterType {
			wasmNetwork = true
			if err := conversion.StructToMessage(typedStruct.Value, wasmNetworkFilterConfig); err != nil {
				return nil, nil, nil, fmt.Errorf("failed to convert extension config struct %+v to Wasm HTTP filter", typedStruct)
			}

		default:
			dv.Set(sv)
		}
		return nil
	}

	if dv.Kind() == sv.Kind() && sv.Type().ConvertibleTo(dv.Type()) {
		dv.Set(sv.Convert(dv.Type()))
		return nil
	}

	// The following conversions use a string value as an intermediate representation
	// to convert between various numeric types.
	//
	// This also allows scanning into user defined types such as "type Int int64".

Rob Pike <******@****.***> 1330061064 +1100

Kuat <******@****.***> 1712018105 -0700

// RUN: stablehlo-quant-opt %s -convert-tf-quant-types -split-input-file -verify-diagnostics | FileCheck %s

// CHECK-LABEL: func @relu_qint8
func.func @relu_qint8(%arg0: tensor<1x!tf_type.qint8>) -> tensor<1x!tf_type.qint8> {
  // CHECK: %[[X:.*]] = "tf.Relu"(%arg0) : (tensor<1xi8>) -> tensor<1xi8>
  %0 = "tf.Relu"(%arg0) : (tensor<1x!tf_type.qint8>) -> tensor<1x!tf_type.qint8>
  func.return %0: tensor<1x!tf_type.qint8>
}

// -----

    // Insert ConvertOp if it does not exist yet. Otherwise, just rewire without
    // creating a ConvertOp.
    for (const OpOperand& connected_op : op.getResult().getUses()) {
      ConvertOp convert_op =
          dyn_cast_or_null<ConvertOp>(connected_op.getOwner());
      // ConvertOp already exists. Rewire the existing convert op into f16.

}

def ConvertFuncToBfloat16Pass : Pass<"stablehlo-convert-func-to-bfloat16", "mlir::func::FuncOp"> {
  let summary = "Convert a StableHLO function to bfloat16";
  let dependentDialects = ["mlir::stablehlo::StablehloDialect"];
}

def ConvertXlaCallModuleOpToBfloat16Pass : Pass<"stablehlo-convert-xla-call-module-op-to-bfloat16", "mlir::func::FuncOp"> {
  let summary = "Convert serialized XlaCallModuleOp to bfloat16";

    tensor_names->insert(coo_sparse.values_tensor_name());
    tensor_names->insert(coo_sparse.indices_tensor_name());
    tensor_names->insert(coo_sparse.dense_shape_tensor_name());
  } else if (tensor_info.has_composite_tensor()) {
    for (const auto& component : tensor_info.composite_tensor().components()) {
      tensor_names->insert(component.name());
    }
  } else {

  auto results = call.getResults();
  auto block_args = entry->getArguments();
  auto yield_args = std::vector<Value>();
  yield_args.insert(yield_args.end(), results.begin(), results.end());
  if (forward_block_args) {
    yield_args.insert(yield_args.end(), block_args.begin(), block_args.end());
  }
  return builder.create<YieldOp>(loc, yield_args);
}

  }

  // Collect all the registered attributes.
  llvm::DenseSet<llvm::StringRef> registered_attrs;
  registered_attrs.insert("name");
  registered_attrs.insert("device");
  for (const auto& attr_def : op_reg_data->op_def.attr()) {
    registered_attrs.insert(attr_def.name());
  }
  // Attributes are not in the registered attributes set will be ignored.
  for (auto& attr : inst->getAttrs()) {