内置的itertools模块具有完美的功能：产物
然后就只需要处理numpy数组了。我发现其中的关键是创建一个布尔值掩码，指示嵌套数组的位置。
我只在3x3矩阵上测试过，但我认为它应该适用于任何数组。

from itertools import product

import numpy as np

array = np.array([[np.array([1, 2]), 0.5, 0.75],
                  [-0.5, np.array([10, 20]), -0.75],
                  [np.array([-1, -2]), 3.5, 3.75]], dtype=object)

# create a vectorized function to find nested arrays
is_cell_an_array = np.vectorize(lambda cell: isinstance(cell, np.ndarray))
# create an array of booleans indicating where the nested cells are
nested_mask = is_cell_an_array(array)

# use itertools.product to generate all the combinations of the nested arrays
for permutation in product(*array[nested_mask]):
    # create a new array
    new_array = np.zeros(array.shape, dtype=float)
    # copy in the unique combination of the nested arrays for this loop
    new_array[nested_mask] = permutation
    # copy in the rest of the values from the original array
    new_array[~nested_mask] = array[~nested_mask]

    print(new_array)

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