code: def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
      if not all(issubclass(cls, t) for t in types):
          return NotImplemented
  
      # For everything else, call the handler:
      fn = cls.handled_ops.get(func.__name__, None)
      if fn:
          return fn(*args, **(kwargs or {}))
      else:
          # Note that here, because we don't need to provide the autograd formulas
          # we can have a default "fallback" that creates a plain Tensor based
          # on the diag elements and calls the func again.
  
          def unwrap(e):
              return (
                  e.diag.diag() if isinstance(e, DiagTensorBelow) else e
              )
  
          def wrap(e):
              if isinstance(e, torch.Tensor) and e.ndim == 1:
                  return DiagTensorBelow(e)
              if (
                  isinstance(e, torch.Tensor)
                  and e.ndim == 2
                  and e.count_nonzero() == e.diag().count_nonzero()
              ):
                  return DiagTensorBelow(e.diag())
              return e
  
          rs = tree_map(
              wrap,
              func(
                  *tree_map(unwrap, args),
                  **tree_map(unwrap, kwargs or {}),
              ),
          )
          return rs
  
imports: import torch
  from copy import deepcopy
  from torch.utils._pytree import tree_map
  from torch.testing._internal.logging_tensor import LoggingTensor
label_desc: Use image node_id 3 for calling the DiagTensorBelow obj's underlying member method code with example usage: obj.__torch_dispatch__(cls, func, types, args, kwargs) and returns: NotImplemented, fn, rs, unknown, e, DiagTensorBelow, DiagTensorBelow