code: def __torch_dispatch__(cls, op, types, args, kwargs=None):
      from .ops import get_qtensor_op_dispatch
  
      # Do not use directly func, but rather its overload
      op = op.overloadpacket
      # Look for a dispatched op accepting QTensor inputs
      qdispatch = get_qtensor_op_dispatch(op)
      if qdispatch is not None:
          if qdispatch.qargs is not None:
              # Check QTensor arguments
              for qarg in qdispatch.qargs:
                  arg = args[qarg.index]
                  if (
                      not isinstance(arg, QTensor)
                      or arg.axis not in qarg.axis
                  ):
                      # Incompatible argument detected: qfallback
                      return qfallback(op, *args, **kwargs)
          return qdispatch.qop(*args, **kwargs)
      # No dispatch available: qfallback
      return qfallback(op, *args, **kwargs)
  
imports: from dataclasses import dataclass
  from typing import Optional
  import torch
  from torch.autograd import Function
  from torch.utils import _pytree
label_desc: Use image node_id 11 for calling the QTensor obj's underlying member method code with example usage: obj.__torch_dispatch__(cls, op, types, args, kwargs) and returns: qfallback, qdispatch, qfallback