issue_comments: 768529007

I've made some mild progress, but it raises a few questions. I've defined this simple Tensor subclass which meets the duck array criteria:

``` class XArrayTensor(torch.Tensor): def new(cls, data=None, requires_grad=False): if data is None: data = torch.Tensor() return torch.Tensor._make_subclass(cls, data, requires_grad)

def __init__(self, data=None, dims: Tuple[str] = None):
    self.dims = dims

def __array_function__(self, func, types, args, kwargs):
    if func not in IMPLEMENTED_FUNCTIONS or not (not all(issubclass(t, torch.Tensor) for t in types)):
        return NotImplemented
    return IMPLEMENTED_FUNCTIONS[func](*args, **kwargs)

def __array_ufunc__(self, func, types, args, kwargs):
    if func not in IMPLEMENTED_FUNCTIONS or not (not all(issubclass(t, torch.Tensor) for t in types)):
        return NotImplementedError
    return IMPLEMENTED_FUNCTIONS[func](*args, **kwargs)

```

where IMPLEMENTED_FUNCTIONS holds a mapping from numpy functions to API compatible tensor operators (similar in style to this)

I added a torch_array_type to pycompat.py, which allows DataArray's .data attribute to persist as an XArrayTensor:

``` xr_tsr = XArrayTensor(torch.rand(3, 2))

data_array = xr.DataArray( xr_tsr, coords=dict(a=["a1", "a2", "a3"], b=["b1", "b1"]), dims=["a", "b"], name="dummy", attrs={"grad": xr_tsr.grad}, ) print(type(data_array.data)) --> yields 'xarray_tensor.XArrayTensor' ```

The issue I'm running into is when I run an operation like np.mean(data_array). The operation gets dispatched to functions within duck_array_ops.py, which are the things I'd like to override.

Also, I'd like to confirm something. If the API matching were complete, would the following be possible?

some_sum = data_array.sum() some_sum.backward() data_array.grad --> provides the gradient

I'm starting to suspect not because that would involve data_array being both DataArray and a Torch.Tensor object. It seems what I'm in fact enabling is that DataArray.data is a Torch.Tensor.