const bool packed_input = partitioned_input.getIsPacked();
    int num_cores_per_replica = partitioned_input.getN();
    if (num_cores_per_replica_attr) {
      num_cores_per_replica = num_cores_per_replica_attr.getInt();
    } else if (packed_input) {
      return partitioned_input->emitOpError()
             << "num cores per replica unavailable";
    }

Status HandleInputOutputArraysWithModule(
    const toco::ModelFlags& model_flags,
    mlir::OwningOpRef<mlir::ModuleOp>* module) {
  mlir::func::FuncOp entry_function = nullptr;
  for (auto func : module->get().getOps<mlir::func::FuncOp>()) {
    if (auto tf_attrs =
            func->getAttrOfType<mlir::DictionaryAttr>("tf.entry_function")) {
      // TODO(b/184697652): There could be multiple entry functions. Let's

  // Apply transformation on each function. For recursive call case, another
  // function can be modified at the same time so avoid running functions in
  // parallel.
  for (auto func : module_op.getOps<func::FuncOp>()) {
    if (failed(applyPatternsAndFoldGreedily(func, frozen_patterns))) {
      func.emitError() << "quant-quantize-weights failed.";
      signalPassFailure();
    }
  }
}

}  // namespace

        final OsCpuObj osCpuObj = new OsCpuObj();
        osObj.cpu = osCpuObj;
        osCpuObj.percent = osProbe.getSystemCpuPercent();
        final OsStats osStats = osProbe.osStats();
        osObj.loadAverages = osStats.getCpu().getLoadAverage();
        return osObj;
    }

    private EngineObj getEngineObj() {
        final EngineObj engineObj = new EngineObj();
        try {

    ArrayRef<int64_t> int64_array);

// Returns the first operation with the given type in the function.
template <typename OpType>
OpType FindOperationOfType(func::FuncOp function) {
  for (auto op : function.getBody().getOps<OpType>()) {
    return op;
  }
  return nullptr;
}

// Returns the first user of the given operation, optionally of the given
// type if provided. If there is no user or user of type, return nullptr.

            function.getArgAttrOfType<mlir::IntegerAttr>(
                i, kResourceArgUniqueIdAttr)) {
      (*func_def.mutable_resource_arg_unique_id())[i] =
          resource_arg_unique_id_attr.getInt();
    }
  }

  return flib_def->AddFunctionDef(std::move(func_def));
}

Status Exporter::Convert(mlir::ModuleOp module,
                         const GraphExportConfig& configs,

            function.getArgAttrOfType<mlir::IntegerAttr>(
                i, kResourceArgUniqueIdAttr)) {
      (*func_def.mutable_resource_arg_unique_id())[i] =
          resource_arg_unique_id_attr.getInt();
    }
  }

  return flib_def->AddFunctionDef(std::move(func_def));
}

Status Exporter::Convert(mlir::ModuleOp module,
                         const GraphExportConfig& configs,

  DCHECK(group_size_attr != nullptr)
      << "module attribute " << kGroupSizeAttrName
      << " is required for CollectiveInfo but not found.";
  int32_t group_key = group_key_attr.getInt();
  int32_t group_size = group_size_attr.getInt();
  VLOG(2) << "Populating CollectiveInfo: group_key=" << group_key
          << " group_size=" << group_size;
  compilation_result->collective_info = {group_key, group_size, 0};

LogicalResult ConvertToI32Attr(IntegerAttr attr, IntegerAttr* attr_i32) {
  if (attr.getType().isInteger(/*width=*/32)) {
    *attr_i32 = attr;
    return success();
  }

  int64_t value = attr.getInt();
  if (value > std::numeric_limits<int>::max() ||
      value < std::numeric_limits<int>::min()) {
    return failure();
  }

  *attr_i32 = IntegerAttr::get(

    }
  }
}

void RaiseTargetSubgraphsPass::runOnOperation() {
  ModuleOp module = getOperation();
  auto& side_effect_analysis = getAnalysis<TF::SideEffectAnalysis>();
  SmallVector<func::FuncOp> funcs(module.getOps<func::FuncOp>());
  int func_count = -1;
  for (auto func : funcs) {
    const auto& info = side_effect_analysis.GetAnalysisForFunc(func);
    for (auto& block : func) {