code: def __torch_function__(cls, func, _, args=(), kwargs=None):
      if kwargs is None:
          kwargs = {}
      if func in expanded_weights_rnn_decomps:
          # in aten, choosing the input or data variants is done by parsing logic. This mimics some of that
          decomp_opts = expanded_weights_rnn_decomps[func]
          use_input_variant = isinstance(
              args[2], list
          )  # data variant uses a list here
          decomp = (
              decomp_opts[0] if use_input_variant else decomp_opts[1]
          )
  
          if decomp is not None:
              with setup_rnn(use_input_variant, args, kwargs):
                  return decomp(*args, **kwargs)
      if func == torch._cudnn_rnn_flatten_weight:
          # since we aren't using the fused cuda kernels for RNNs, don't do this
          return
      if func in cls.handled_functions:
          return cls.handled_functions[func].apply(
              tuple(kwargs.keys()),
              func,
              *(args + tuple(kwargs.values())),
          )
      # We cannot use a fallback here because we do not know the batch dimension for any regular tensor inputs,
      # i.e. torch.add(torch.Tensor, ExpandedWeight)
      raise RuntimeError(
          f"Expanded Weights encountered but cannot handle function {func.__name__}"
      )
  
imports: from contextlib import contextmanager
  import torch
  import functools
  from torch._decomp import decomposition_table
  from typing import Callable, Dict
  from torch.utils._pytree import tree_map_only
label_desc: Use image node_id 3 for calling the ExpandedWeight obj's underlying member method code with example usage: obj.__torch_function__(cls, func, _, args, kwargs) and returns: cls, decomp