code: def infer_filter_per_worker(data: Any) -> bool:
      out = True
      if isinstance(data, (Data, HeteroData)) and data.is_cuda:
          out = False
      logging.debug(
          f"Inferred 'filter_per_worker={out}' option for feature "
          f"fetching routines of the data loader"
      )
      return out
  
imports: import copy
  import logging
  import math
  from typing import Any, Dict, Optional, Tuple, Union
  import numpy
  import torch
  from torch import Tensor
  import torch_geometric.typing
  from torch_geometric import EdgeIndex
  from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData, TensorAttr, remote_backend_utils
  from torch_geometric.data.storage import EdgeStorage, NodeStorage
  from torch_geometric.typing import EdgeType, FeatureTensorType, InputEdges, InputNodes, NodeType, OptTensor, SparseTensor, TensorFrame
label_desc: Use image node_id 10 for calling a global function with example usage: infer_filter_per_worker(data) and returns: out