code: def all(
      x: torch.Tensor,
      /,
      *,
      axis: Optional[Union[int, Sequence[int]]] = None,
      keepdims: bool = False,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      x = x.type(torch.bool)
      if axis is None:
          num_dims = len(x.shape)
          axis = list(range(num_dims))
      if isinstance(axis, int):
          return torch.all(x, dim=axis, keepdim=keepdims, out=out)
      dims = len(x.shape)
      axis = [i % dims for i in axis]
      axis.sort()
      for i, a in enumerate(axis):
          x = torch.all(
              x, dim=a if keepdims else a - i, keepdim=keepdims, out=out
          )
      return x
  
label_desc: Use image node_id 1 for calling a global function with example usage: all() and returns: x, torch