np.concatenate with empty arrays yields unexpected shape issues in NumPy 1.24
I'm building a feature where I'm stuck trying to I'm migrating some code and I've been struggling with this for a few days now and could really use some help... I'm working with a perplexing scenario with `np.concatenate` when trying to combine a set of NumPy arrays, some of which are empty. I expect the result to maintain the same shape structure as the non-empty arrays, but I am seeing unexpected behavior. Here's a simplified version of the code I'm using: ```python import numpy as np a = np.array([[1, 2], [3, 4]]) b = np.array([]) c = np.array([[5, 6]]) result = np.concatenate((a, b, c)) print(result.shape) ``` When I run this, it raises a `ValueError` stating: ``` ValueError: all the input arrays must have same number of dimensions ``` I understand that `b` is empty and does not have the same shape as `a` and `c`, but I'm not sure how to handle such cases gracefully. I've tried checking shapes before concatenation and using `np.vstack` or `np.array` to ensure uniformity, but it still fails. Is there a reliable way to concatenate multiple arrays while safely ignoring empty ones without causing errors? Any best practices or tips would be greatly appreciated! For context: I'm using Python on Linux. Any ideas what could be causing this? This is for a microservice running on Debian. Am I missing something obvious? I recently upgraded to Python latest. Cheers for any assistance! My team is using Python for this mobile app. I'd love to hear your thoughts on this.