mlir::IntegerAttr num_cores_per_replica_attr =
      cluster->getAttrOfType<mlir::IntegerAttr>(kNumCoresPerReplicaAttr);
  if (num_cores_per_replica_attr) {
    return num_cores_per_replica_attr.getInt();
  } else {
    return 1;
  }
}

// Get the TPUDevicesAndHosts for a cluster that is not replicated.
mlir::LogicalResult GetTPUDevicesAndHostsNotReplicated(

    return switchn.emitOpError() << "expects a `num_outs` integer attribute";

  // Expect num_outs results + 1 control output.
  if (switchn.getNumResults() != num_outs.getInt() + 1)
    return switchn.emitOpError()
           << "expect `num_outs` (" << num_outs.getInt() << ") results but got "
           << (switchn.getNumResults() - 1);

  // Check that operand can be broadcasted to each output type.

            import org.gradle.declarative.dsl.model.annotations.Restricted;


            @Restricted
            public interface LibraryDependencies extends Dependencies {

                DependencyCollector getApi();

                DependencyCollector getImplementation();

                DependencyCollector getRuntimeOnly();

                DependencyCollector getCompileOnly();

      assert(id_attr &&
             "tf.resource_arg_unique_id attribute should exist on either "
             "none or all arguments.");
      auto emplace_res = attr_id_to_internal_id.try_emplace(
          id_attr.getInt(), next_unique_instance_id);
      AddValueUniqueIDMapping(arg, emplace_res.first->getSecond());
      // Only increment ID if it has been used.
      if (emplace_res.second) ++next_unique_instance_id;
    }
  } else {

  llvm::SmallSet<int, 4> dynamic_arg_idx_set;
  if (dynamic_arg_idx) {
    for (auto idx : dynamic_arg_idx.getValue()) {
      dynamic_arg_idx_set.insert(mlir::dyn_cast<IntegerAttr>(idx).getInt());
    }
  }

  for (auto operand_type_and_idx : llvm::enumerate(op.getOperandTypes())) {
    Type operand_type = operand_type_and_idx.value();
    int index = operand_type_and_idx.index();

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,