code: def split_kwargs(
      kwargs: Dict[str, Any],
      sampler_cls: Type,
  ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
      r"""Splits keyword arguments into sampler and loader arguments."""
      sampler_args = inspect.signature(sampler_cls).parameters
  
      sampler_kwargs: Dict[str, Any] = {}
      loader_kwargs: Dict[str, Any] = {}
  
      for key, value in kwargs.items():
          if key in sampler_args:
              sampler_kwargs[key] = value
          else:
              loader_kwargs[key] = value
  
      return sampler_kwargs, loader_kwargs
  
imports: import copy
  import inspect
  import warnings
  from typing import Any, Dict, Optional, Tuple, Type, Union
  import torch
  from torch_geometric.data import Data, Dataset, HeteroData
  from torch_geometric.loader import DataLoader, LinkLoader, NodeLoader
  from torch_geometric.sampler import BaseSampler, NeighborSampler
  from torch_geometric.typing import InputEdges, InputNodes, OptTensor
label_desc: Use image node_id 3 for calling a global function with example usage: split_kwargs(kwargs, sampler_cls) and returns: sampler_kwargs,  loader_kwargs