}

// -----

module attributes {tf_saved_model.semantics} {
  func.func @conv_with_filter_larger_than_1MB(%arg0: tensor<1x224x224x3xf32> {tf_saved_model.index_path = ["input"]}) -> (tensor<1x224x112x512xf32> {tf_saved_model.index_path = ["output"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {

            representative_datasets=[
                qc.RepresentativeDatasetConfig(
                    tf_record=qc.TfRecordFile(path=dataset_path)
                )
            ]
        ),
        tf_saved_model=qc.TfSavedModelConfig(tags=[tag_constants.SERVING]),
        pipeline_config=qc.PipelineConfig(
            merge_fusion_with_dequantize=merge_fusion_with_dequantize
        ),
    )

// RUN:     -stablehlo-quantize-composite-functions=enable-per-channel-quantized-weight=false | FileCheck --check-prefix=CHECK-PER-TENSOR %s

// Tests that basic dot_general is properly quantized.

module attributes {tf_saved_model.semantics} {
  func.func private @quantize_dot_general_fn(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {

}

// TODO(b/308773062): Add whole_model unit-test

// -----

module {
  func.func @matmul2_with_int_per_layer(%arg0: tensor<2x2xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> tensor<2x2xf32> {
    %cst = "tf.Const"() {device = "", value = dense<[[-0.630731344, 0.54962182], [0.180364341, -0.764542698]]> : tensor<2x2xf32>} : () -> tensor<2x2xf32>

namespace errors = tensorflow::errors;
namespace tfl = mlir::TFL;

namespace {

constexpr char kScatterRegionFuncName[] = "update_computation_func_name";

using ::mlir::tf_saved_model::kTfSavedModelExportedNamesAttr;
using ::mlir::tf_saved_model::kTfSavedModelIndexPathAttr;
using ::tflite::IsValidBufferOffset;

// Create the MLIR NamedLoc location corresponding to a given tensor