mlir::Builder builder(ctx);

  // Collect devices with attached metadata.
  llvm::SmallVector<mlir::NamedAttribute, 8> devices;
  devices.reserve(device_set->devices().size());

  // For device that do not have any metadata, or if we failed to parse metadata
  // from the DeviceSet, we add a unit attribute to the `tf.devices` attribute.
  for (Device* device : device_set->devices()) {

    }

    auto layout = mlir::GetTPUInfeedLayout(
        mlir::TupleType::get(builder.getContext(), result_types), builder);
    if (failed(layout)) return mlir::WalkResult::interrupt();
    // Do not append a UnitAttr for the "token" operand here to avoid
    // compilation failure when exporting the "layouts" attribute to a graph
    // node. Instead, add the UnitAttr during LegalizeTF pass.

  if (absl::Status status = AddCalibrationStatistics(
          module_op, calibration_data_dir, config.calibration_options(),
          *py_function_lib_);
      !status.ok()) {
    LOG(WARNING) << "Some CustomAggregator ops do not have min or max "
                    "values. Parts of the graph are not quantized. "
                 << status;
  }

  return module_op;
}

==============================================================================*/

// This file wraps TensorFlow Graph(Def) to MLIR module conversion into passes
// to satisfy the API of MLIR pass registration. In order to do this, the
// command-line option header is pulled in.

#include <memory>
#include <utility>

#include "absl/container/flat_hash_set.h"
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project

    rewriter.eraseOp(call_op);
    func_op.eraseResult(0);
    func_op.insertResult(0, new_call_op.getResult(0).getType(),
                         /*resultAttrs=*/nullptr);

    // Modify the quantized fused function to do dequantize+relu(6).
    rewriter.setInsertionPoint(req_op);
    Value new_result = rewriter.create<mlir::stablehlo::UniformDequantizeOp>(
        req_op.getLoc(), func_op.getResultTypes()[0], req_op.getOperand());

  // First, we need to convert from functional, to executor dialect.
  pm.addNestedPass<FuncOp>(
      mlir::CreateFunctionalToExecutorDialectConversionPass());

  // Do a single pass to split the graph's single island op into an island per
  // op as expected by the following passes.
  pm.addNestedPass<FuncOp>(mlir::TF::CreateSplitIntoIslandPerOpPass());

  }
  return it->second.potentially_written;
}

// Analyze the specified region for resource mutating operations, namely
// TF::AssignVariableOp, if so, set the resource associated as "potentially
// written". Do this recursively across the chain of regions via call or
// control flow ops.
// TODO(ashwinm): Move to iterative traversal.
LogicalResult ResourceAnalyzer::AnalyzeRegion(Region& region) {
  // Avoid infinite recursion.

using ::tensorflow::SignatureDef;
using ::tensorflow::quantization::PyFunctionLibrary;

// Note for maintainers: the definitions should ONLY mirror existing functions
// defined in different targets. Do not include any extra business logic that
// causes divergence from the semantics of mirrored functions.

absl::Status PywrapQuantizeStaticRangePtq(
    absl::string_view src_saved_model_path,

        TF::TensorFlowDialect::getDialectNamespace())
      return failure();

    // Refining types may have implications to the attached regions or symbol
    // references so do not update such ops.
    if (!op->getRegions().empty() || HasSymbolRefAttr(op)) return failure();

    IRMapping mapper;
    bool has_type_change = false;

// TODO - b/296503614: [Converter Component][TF-Quantizer] Reflect custom traits
// from TF-Quantizer to stableHLO quantization
bool IsOpWithDataMovementTrait(Operation* op) {
  // Supported data movement ops. These ops do not perform any computations and
  // has one result operand.
  return isa<TF::IdentityOp, TF::CastOp, TF::ReshapeOp, TF::XlaShardingOp,
             TF::GatherOp, TF::GatherV2Op, TF::XlaGatherOp, TF::ExpandDimsOp,