Subclassing Numpy Array - Propagate Attributes

I would like to know how custom attributes of numpy arrays can be propagated, even when the array passes through functions like np.fromfunction.

For example, my class ExampleTensor defines an attribute attr that is set to 1 on default.

import numpy as np

class ExampleTensor(np.ndarray):
    def __new__(cls, input_array):
        return np.asarray(input_array).view(cls)

    def __array_finalize__(self, obj) -> None:
        if obj is None: return
        # This attribute should be maintained!
        self.attr = getattr(obj, 'attr', 1)

Slicing and basic operations between ExampleTensor instances will maintain the attributes, but using other numpy functions will not (probably because they create regular numpy arrays instead of ExampleTensors). My question: Is there a solution that persists the custom attributes when a regular numpy array is constructed out of subclassed numpy array instances?

Example to reproduce problem:

ex1 = ExampleTensor([[3, 4],[5, 6]])
ex1.attr = "some val"

print(ex1[0].attr)    # correctly outputs "some val"
print((ex1+ex1).attr) # correctly outputs "some val"

np.sum([ex1, ex1], axis=0).attr # Attribute Error: 'numpy.ndarray' object has no attribute 'attr'

from Subclassing Numpy Array - Propagate Attributes