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 {

  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)))) {

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));

                               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) {

  // Then branch gets the data and calls the reduce_function.
  auto& then_branch = dataset_if.getThenBranch();
  then_branch.push_back(new Block);
  builder.setInsertionPointToEnd(&then_branch.front());
  // Add iterator operational data access inside if.
  auto get_value = builder.create<TF::OptionalGetValueOp>(loc, dataset_types,
                                                          get_next.getResult());

      });
      return false;
    }
    return true;
  }
};

void FusedKernelMatcherPass::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  auto func = getOperation();
  patterns.add<FuseConv2DBiasAdd, FuseMatMulBiasAdd>(&getContext());

  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
}

}  // namespace

    // Insert all ops including nested ops for checking outputs/side effects.
    cluster_op.walk(
        [&](Operation* op) { tail_outside_compiled_ops_set.insert(op); });

    // Only add top level ops to output vector.
    tail_outside_compiled_ops->push_back(&cluster_op);
  }

  *cluster_results = cluster_results_set.takeVector();
}

    return success();
  }
};

void SessionInitializerOp::getCanonicalizationPatterns(
    RewritePatternSet &results, MLIRContext *context) {
  results.add<OptimizeSessionInitializerPattern>(context);
}

SmallVector<StringRef, 2> GetSessionInitializerExportedName(ModuleOp op) {
  auto session_initializer_op = GetSessionInitializerOp(op);

        TypeID::get<TF::XlaSpmdFullToShardShapeOp>(),
        TypeID::get<TF::XlaSpmdShardToFullShapeOp>(),
        TypeID::get<TF::XlaSvdOp>(),
    };

    // Add the ops from the TPUEmbeddingOpsRegistry.
    for (auto op_type_id :
         TF::TPUEmbeddingOpsRegistry::Global().GetOpsTypeIds()) {
      ops_set->insert(op_type_id);
    }
    return ops_set;
  }();