const std::optional<DictionaryAttr>& devices) {
  auto result = region.walk([&](Operation* op) -> WalkResult {
    if (RequiresReplicaIDAttribute(op)) {
      op->setAttr(kReplicaIdAttr, builder.getI64IntegerAttr(replica_id));
      return WalkResult::advance();
    }

    if (auto placeholder = dyn_cast<TF::_TPUDeviceOrdinalPlaceholderOp>(op)) {
      int64_t device_ordinal;

    }

    public void test_toString() {
        String tag = "tag", id = "id", css = "css", attrName = "attrName", attrValue = "attrValue";
        PrunedTag prunedtag = new PrunedTag(tag);
        prunedtag.setAttr(attrName, attrValue);
        prunedtag.setId(id);
        prunedtag.setCss(css);
        assertEquals("PrunedTag [tag=" + tag + ", id=" + id + ", css=" + css + ", attrName=" + attrName + ", attrValue=" + attrValue + "]",

      op_to_update->setAttr(kDeviceAttr, new_device_attr);
      PopulateDeviceForOpResults(*op_to_update, new_device_attr.getValue(),
                                 value_to_device);

      if (auto sink =
              llvm::dyn_cast<tf_executor::NextIterationSinkOp>(op_to_update)) {
        auto source = sink.GetSource();
        source->setAttr(kDeviceAttr, new_device_attr);

    } else {
      auto handle = resource_tensor.scalar<tensorflow::ResourceHandle>()();
      is_variable_initialized =
          IsVariableInitialized(var_op, handle.device(), mgr, session);
    }
    var_op->setAttr("_is_initialized",
                    BoolAttr::get(context, is_variable_initialized));
  }
  return success();
}

LogicalResult MarkInitializedVariablesInFunction(ModuleOp module,

      auto float_attr =
          mlir::FloatAttr::get(float_type, static_cast<float>(cost));
      device_costs.push_back({device_identifier, float_attr});
    }

    op->setAttr("per_device_costs",
                mlir::DictionaryAttr::get(module.getContext(), device_costs));
  });
}

}  // namespace

//////////// Importer ////////////

namespace tensorflow {

void AddControlInput(TF_Graph* graph, TF_Operation* op, TF_Operation* input);

// Changes an attr value in the node_def Protocol Buffer and sets a status upon
// completion.
void SetAttr(TF_Graph* graph, TF_Operation* op, const char* attr_name,
             TF_Buffer* attr_value_proto, TF_Status* status);

// Clears the attr in the node_def Protocol Buffer and sets a status upon
// completion.

          // Set interface_name & target to the call_op as well.
          new_call->setAttr(kInterfaceNameAttr,
                            builder->getStringAttr(interface_name));
          new_call->setAttr(
              kDevice,
              builder->getStringAttr(preferred_inference_device_type.hardware));
          new_call->setAttr(
              kInferenceType,
              builder->getStringAttr(GetInferenceString(

  Builder builder(&getContext());

  // Adds the `tf._noinline = true` attribute to the function if the name
  // matches.
  if (noinline_functions.contains(func_op.getSymName())) {
    func_op->setAttr(kTfNoinlineAttr, builder.getBoolAttr(true));
    LLVM_DEBUG(llvm::dbgs()
               << "Marked tf._noinline = true: " << func_op.getSymName());
  }
}

static PassRegistration<MarkFunctionsNoinlinePass> pass{};

            !HasOutsideCompiledAncestor(input_defining_op) &&
            !input_defining_op->hasAttrOfType<StringAttr>(
                kXlaOutsideCompilationAttr)) {
          input_defining_op->setAttr(
              kXlaOutsideCompilationAttr,
              StringAttr::get(input_defining_op->getContext(), "auto"));
          outside_compiled_ops.push(input_defining_op);
        }
      }

      /*disabled_checks=*/empty_array_attr);
  xla_call_module_op->setAttr(TF::kStablehloEntryFunctionAttrName,
                              SymbolRefAttr::get(stablehlo_func_op));
  // Set jax.uses_shape_polymorphism=true to enable shape refinement at runtime.
  // This is needed for native serialization version >= 8.
  xla_call_module_op->setAttr(
      kStablehloModuleAttrsAttrName,