Handling dtype conflicts when using np.apply_along_axis with custom functions
I'm working with a dtype conflict when using `np.apply_along_axis` in NumPy with a custom function that modifies the data type of the output. My function is intended to process a 2D array and return a 1D array, and I'm using `np.float64` as the desired output type. However, when I apply the function, I get the following behavior: `ValueError: setting an array element with a sequence`. Here’s my code: ```python import numpy as np def custom_function(row): return row.sum() / len(row) arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32) result = np.apply_along_axis(custom_function, 1, arr) print(result) ``` I expect `result` to be an array of type `np.float64`, but it appears to be an array of type `np.int32`. Additionally, if I modify the custom function to return a list instead of a single float, I see the same `ValueError` mentioned earlier. I’ve also tried explicitly specifying the output dtype in the `apply_along_axis`, like so: ```python result = np.apply_along_axis(custom_function, 1, arr).astype(np.float64) ``` But that didn’t resolve the scenario. Does anyone have insights on how to correctly handle the dtype when using `np.apply_along_axis` with a function that returns a different data type? What am I missing here? Any suggestions would be greatly appreciated! My development environment is Linux. This is part of a larger service I'm building. What's the best practice here?