code: def __call__(cls, *args: Any, **kwargs: Any) -> Any:
      base_cls = kwargs.pop("_base_cls", Data)
  
      if issubclass(base_cls, Batch):
          new_cls = base_cls
      else:
          name = f"{base_cls.__name__}{cls.__name__}"
  
          # NOTE `MetaResolver` is necessary to resolve metaclass conflict
          # problems between `DynamicInheritance` and the metaclass of
          # `base_cls`. In particular, it creates a new common metaclass
          # from the defined metaclasses.
          class MetaResolver(type(cls), type(base_cls)):  # type: ignore
              pass
  
          if name not in globals():
              globals()[name] = MetaResolver(name, (cls, base_cls), {})
          new_cls = globals()[name]
  
      params = list(
          inspect.signature(base_cls.__init__).parameters.items()
      )
      for i, (k, v) in enumerate(params[1:]):
          if k == "args" or k == "kwargs":
              continue
          if i < len(args) or k in kwargs:
              continue
          if v.default is not inspect.Parameter.empty:
              continue
          kwargs[k] = None
  
      return super(DynamicInheritance, new_cls).__call__(
          *args, **kwargs
      )
  
imports: import inspect
  from collections.abc import Sequence
  from typing import Any, List, Optional, Type, Union
  import numpy
  import torch
  from torch import Tensor
  from typing_extensions import Self
  from torch_geometric.data.collate import collate
  from torch_geometric.data.data import BaseData, Data
  from torch_geometric.data.dataset import IndexType
  from torch_geometric.data.separate import separate
label_desc: Use image node_id 1 for calling the DynamicInheritance obj's underlying member method code with example usage: obj.__call__(cls) and returns: super