config.mutable_calibration_options()
        ->mutable_representative_datasets()
        ->Add(preset_datasets.begin(), preset_datasets.end());
  }

  // Create a new `QuantizationSpecs` to replace the existing one. The
  // expansion from `StaticRangePtqPreset` gets populated first and then
  // user-provided explicit `QuantizationSpec`s will be appended.
  QuantizationSpecs new_specs{};

    return convert(attr);
  }
  return nullptr;
}

DenseElementsAttr UniformQuantizedPerAxisValueConverter::convert(
    DenseFPElementsAttr attr) {
  // Creates the converter for each chunk. Normally the size of the
  // quantization dim is 3, so we can cache all the converters.
  ShapedType type = attr.getType();
  std::size_t dim_size = type.getDimSize(quantization_dim_);

        dyn_cast<arith::ConstantOp>(op.getOperand(0).getDefiningOp());
    if (!input_op) {
      return failure();
    }

    Builder builder(op.getContext());
    auto new_gather_op = rewriter.create<TFL::GatherOp>(
        op.getLoc(),
        /*result=*/
        mlir::cast<TensorType>(op.getResult().getType())
            .clone(builder.getI4Type()),
        /*operand=*/op.getOperands(), op->getAttrs());

    } else {
      /* If we're not running on a TPU node, we might not be able to
       * actually call the part of the TPU API that gives us layout.
       * This happens e.g. for unit tests. Below we just create a reasonable
       * layout.  We sort by dimension size, which makes the layout agree with
       * the "correct" TPU layout in surprisingly many cases.
       * Note that the corresponding InfeedEnqueue op will be generated

using tensorflow::tracing::TracingTensorHandle;

void TF_SetTracingImplementation(const char* name, TF_Status* s) {
  tsl::Set_TF_Status_from_Status(s, SetDefaultTracingEngine(name));
}

// Creates a new TensorFlow function, it is an execution context attached to a
// given tracing context.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* s) {
  return wrap(CreateTracingExecutionContext(fn_name, s));

}

// `DumpTensor` op saves entire value of input to as a tensor proto into a
// specified directory and filename. When enabled is set to false, op is
// disabled and won't save any value. It also creates `QuantizationUnit` proto
// with `func_name` and `node_name` to identify the op.
REGISTER_OP("DumpTensor")
    .Input("tensor_data: T")
    .Attr("log_dir_path: string")
    .Attr("file_name: string")
    .Attr("T: type")

    module_op.emitError() << "quant-convert-tpu-model-to-cpu pattern "
                             "conversion did not converge.";
    signalPassFailure();
    return;
  }
}

}  // namespace

// Creates an instance of `ConvertTpuModelToCpuPass`.
std::unique_ptr<OperationPass<ModuleOp>> CreateConvertTpuModelToCpuPass() {
  return std::make_unique<ConvertTpuModelToCpuPass>();
}

#include "tensorflow/compiler/jit/xla_compilation_cache.pb.h"
#include "xla/service/hlo.pb.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/public/session.h"

namespace tensorflow {
namespace {

// Creates a float tensor of linearly increasing values, starting from offset.
Tensor CreateInputTensor(const TensorShape& shape, float offset) {
  Tensor tensor(DT_FLOAT, shape);
  for (int64 i = 0; i < tensor.flat<float>().size(); ++i) {

            ctx);
    if (failed(applyPatternsAndFoldGreedily(func_op, std::move(patterns_2)))) {
      signalPassFailure();
    }
  }
}

}  // namespace

// Creates an instance of the TensorFlow dialect PrepareQuantize pass.
std::unique_ptr<OperationPass<ModuleOp>> CreatePrepareQuantizePass(
    const bool enable_per_channel_quantized_weight, const int bit_width) {

    if (failed(ApplyQuantizationSpec(spec, module_op))) {
      signalPassFailure();
      return;
    }
  }
}

}  // namespace

// Creates `LiftQuantizableSpotsAsFunctionsPass` with user-defined
// `QuantizationSpecs`.
std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsPass(
    const QuantizationSpecs& quantization_specs) {