if (memory_bandwidth >= 0) {
        builder.set_memory_bandwidth(memory_bandwidth);
      }
    }
    // TODO(annarev): Add gflops field in DeviceDescription and set it here.
    // TODO(annarev): Perhaps add `supports_unified_memory` in
    // DeviceDescription.
    return builder.Build();
  }

  absl::StatusOr<std::unique_ptr<Event>> CreateEvent() override {

    // fixed_point = clamp(clamp_min, clamp_max, (
    //   roundHalfToEven(expressed / scale) + zero_point))
    APFloat scaled = (expressed_value / scale_);
    scaled.roundToIntegral(round_mode_);
    scaled.add(zero_point_, round_mode_);
    APFloat fixed_point = llvm::minimum(scaled, clamp_max_);
    fixed_point = llvm::maximum(fixed_point, clamp_min_);

    llvm::APSInt result(storage_bit_width_, !is_signed_);

// limitations under the License.

// Revision History
// Version 0: Initial version.
// Version 1: Add subgraphs to schema.
// Version 2: Rename operators to conform to NN API.
// Version 3: Move buffer data from Model.Subgraph.Tensors to Model.Buffers.
// Version 3a: Add new builtin op code field. Has backward compatibility with
//             version 3.

                       graph_op_def.deprecation().version(), ":\n",
                       graph_op_def.deprecation().explanation(), ".\n");
  } else if (api_def.summary().empty()) {
    comment = "TODO: add doc.\n";
  } else {
    comment = strings::StrCat(api_def.summary(), "\n");
  }
  if (!api_def.description().empty()) {
    strings::StrAppend(&comment, "\n", api_def.description(), "\n");
  }

  OperationState new_state(loc, tf_op_name, inputs, output_types, attr_list);
  Operation* new_op = rewriter.create(new_state);
  if (materialize_derived_attrs_) {
    for (const auto& attr : derived_attrs) {
      // Add or update the derived attribute with the value. Skip the fixed
      // element type attributes, in case they are present in the NodeDef.
      if (!fixed_elt_type_attrs_.contains(attr.first())) {

};

void DeferActivationTransposePass::runOnOperation() {
  func::FuncOp func_op = getOperation();
  MLIRContext& ctx = getContext();

  RewritePatternSet patterns(&ctx);
  patterns.add<DeferActivationTransposeForAddOp,
               DeferActivationTransposeForMaxPoolReduceWindowOp,
               DeferActivationTransposeForMaxOp>(&ctx);
  if (failed(applyPatternsAndFoldGreedily(func_op, std::move(patterns)))) {

      for (const auto& enumerated_result :
           llvm::enumerate(candidate_op->getResults())) {
        Value result = enumerated_result.value();
        Type result_type = result.getType();
        // Add this to the test coverage once we create test ops with none type
        // results.
        if (mlir::isa<NoneType>(result_type)) {
          outputs_replaced.insert({result, enumerated_result.index()});

void PrepareQuantizeDRQPass::runOnOperation() {
  MLIRContext* ctx = &getContext();
  RewritePatternSet patterns(ctx);
  ModuleOp module_op = getOperation();

  populateWithGenerated(patterns);
  patterns.add<PrepareDRQQuantizableOp>(ctx, quant_specs_, op_set_,
                                        enable_per_channel_quantization_);
  FrozenRewritePatternSet frozen_patterns(std::move(patterns));

    const auto& api_def_attr(op_info.api_def.attr(i));
    // Skip inferred arguments
    if (op_info.inferred_input_attrs.count(attr.name()) > 0) continue;
    // Skip if it has default value (TODO(unda, b/249345399): add our custom
    // values)
    if (api_def_attr.has_default_value()) continue;
    // TODO(unda, b/253432797): handle unimplemented input attribute types

                               OpType& found_op) {
  // Find the TPUReplicationMetadata or TPUCompilationResult ops which will be
  // cloned/inserted into each region. We add them to the merged_set so that
  // they're ignored when extracting the four main functions.
  found_op = nullptr;
  for (OpType op : func.getOps<OpType>()) {
    if (found_op != nullptr) {