}
      return r;
    }});

  render();

  // Helper functions follow:

  // hilite changes the highlighting of elements corresponding to specified src.
  function hilite(src, on) {
    if (on) {
      matches.add(src);
    } else {
      matches.delete(src);
    }
    toggleClass(src, 'hilite', on);
    return true;
  }

  // Display action menu (triggered by right-click on a frame)

  line-height: 18px;
  cursor: default;
  font-family: "Google Sans", Arial, sans-serif;
  font-size: 12pt;
  z-index: 2;
}
/* Box highlighting via shadows to avoid size changes */
.hilite { box-shadow: 0px 0px 0px 2px #000; z-index: 1; }
.hilite2 { box-shadow: 0px 0px 0px 2px #000; z-index: 1; }
/* Gap left between callers and callees */
.separator {
  position: absolute;
  text-align: center;
  font-size: 12pt;
  font-weight: bold;

  function setNodeHighlight(n, set) {
    if (options && options.hiliter) return options.hiliter(n, set);

    const elem = document.getElementById('node' + n);
    if (!elem) return false;

    // Handle table row highlighting.
    if (elem.nodeName == 'TR') {
      elem.classList.toggle('hilite', set);
      return true;
    }

    // Handle svg element highlighting.

#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/debug/debug.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_export.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/tf_tfl_passes.h"

#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/lite/core/model_builder.h"
#include "tensorflow/lite/tools/optimize/reduced_precision_support.h"

namespace mlir {
namespace lite {
namespace {


TEST(SparsifyModelTest, MetadataIsAddedToOutputModel) {
  std::string expected_key = tflite::optimize::kTfLiteReducedPrecisionKey;

#include "absl/container/flat_hash_set.h"
#include "flatbuffers/flatbuffer_builder.h"  // from @flatbuffers
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/lite/c/c_api_types.h"
#include "tensorflow/lite/model.h"

namespace mlir {
namespace lite {

// Supported resulting types from quantization process.
enum class BufferType { QUANTIZED_INT8, QUANTIZED_FLOAT16 };

#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/op_stat_pass.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/stablehlo_util.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/transforms.h"
#include "tensorflow/compiler/mlir/lite/tf_tfl_passes.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"

#include "mlir/Parser/Parser.h"  // from @llvm-project
#include "mlir/Support/FileUtilities.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/flatbuffer_export.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {

absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> ImportFlatbufferOrMlir(

        } else {
          return tflite::TensorType_INT4;
        }
      case 8:
        return itype.isUnsigned() ? tflite::TensorType_UINT8
                                  : tflite::TensorType_INT8;
      case 16:
        return itype.isUnsigned() ? tflite::TensorType_UINT16
                                  : tflite::TensorType_INT16;
      case 32:
        return itype.isUnsigned() ? tflite::TensorType_UINT32

#include "tensorflow/compiler/mlir/lite/metrics/error_collector.h"
#include "tensorflow/compiler/mlir/lite/metrics/types_util.h"
#include "tensorflow/lite/python/metrics/converter_error_data.pb.h"

namespace mlir {
namespace TFL {

// Collects errors when running the pass manager.
class ErrorCollectorInstrumentation : public PassInstrumentation {
  using ConverterErrorData = tflite::metrics::ConverterErrorData;