you can use `numpy.reshape(ndarray, shape)` and `arraymancer.reshape(tensor, shape)`. The module name can serve as namespacing prefix when calling functions.
Also your object types PyObject vs Tensor should disambiguate what to call for the compiler so I would be surprised if there was conflict, do you have an example to reproduce? I personally didn't look into Numpy interop yet. My plan was to introduce zero-copy interop with Numpy once this PR lands [https://github.com/mratsim/Arraymancer/pull/420](https://github.com/mratsim/Arraymancer/pull/420) but it's being blocked by a Nim bug: [https://github.com/nim-lang/Nim/issues/13193](https://github.com/nim-lang/Nim/issues/13193) That said, Arraymancer supports both read and write from .npy files, see tests: [https://github.com/mratsim/Arraymancer/blob/28a0a255/tests/io/test_numpy.nim](https://github.com/mratsim/Arraymancer/blob/28a0a255/tests/io/test_numpy.nim). The only limitation is that you need to pass the data type. let a = read_npy[int64](filePathIn) a.write_npy(filePathOut) Run
