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This was discussed on slack. There are errors like
FAILED array_api_tests/test_signatures.py::test_func_signature[max] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[mean] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.float32 with fill value 0.0
FAILED array_api_tests/test_signatures.py::test_func_signature[min] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[prod] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[std] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.float32 with fill value 0.0
FAILED array_api_tests/test_signatures.py::test_func_signature[sum] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.uint8 with fill value 0
FAILED array_api_tests/test_signatures.py::test_func_signature[var] - hypothesis.errors.InvalidArgument: Could not create full array of dtype=torch.float32 with fill value 0.0
which are not easy to read
The problem is that hypothesis tries to pass an integer to full, but torch only supports a tuple. I think a simple fix would be if hypothesis only tried passing a tuple to full to avoid this issue.
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