issue_comments: 765905229

This data as json

html_url issue_url id node_id user created_at updated_at author_association body reactions performed_via_github_app issue
https://github.com/pydata/xarray/issues/3232#issuecomment-765905229 https://api.github.com/repos/pydata/xarray/issues/3232 765905229 MDEyOklzc3VlQ29tbWVudDc2NTkwNTIyOQ== 98330 2021-01-23T10:57:48Z 2021-01-23T11:09:52Z NONE

Create a custom subclass of PyTorch's Tensors which meets the duck array required methods and attributes. Since this isn't officially supported, looks like I could run into issues getting this subclass to persist through tensor operations.

If you use PyTorch 1.7.1 or later, then Tensor subclasses are much better preserved through pytorch functions and operations like slicing. So a custom subclass, adding the attributes and methods Xarray requires for a duck array should be feasible.

data = as_compatible_data(data)

Looks like you need to patch that internally just a bit, probably adding pytorch to NON_NUMPY_SUPPORTED_ARRAY_TYPES.

Note that I do not expect anymore that we'll be adding __array_function__ to torch.Tensor, and certainly not any time soon. My current expectation is that the "get the correct namespace from an array/tensor object directly" from https://numpy.org/neps/nep-0037-array-module.html#how-to-use-get-array-module and https://data-apis.github.io/array-api/latest/ will turn out to be a much better design long-term.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   482543307