When we add an array and a list together, the list is also treated as an array. However, we might not want the addition of a list and an array to yield the same result as the addition of two arrays.
Complete the custom_sum() function that takes two arguments. The two arguments can only be of the following types:
Two NumPy arrays;
A NumPy array and a list;
Two lists.
In the first case, sum them and return the result. In the second case, return a string with the warning One argument is a list, and in the last case, return Both arguments are lists, not arrays.
All lists and arrays will be one-dimensional and of the same size. Note that you don't have to use any print() statements in this task.
Below is an example of the input and the expected outputs:
Input: [1, 2, 3], [4, 5, 6]
Expected Output: 'Both arguments are lists, not arrays'
Input: np.array([1, 2, 3]), [4, 5, 6]
Expected Output: 'One argument is a list'
Input: np.array([1, 2, 3]), np.array([4, 5, 6])
Expected Output: array([5, 7, 9])Tip: You can check the variable type by calling isinstance(arg, type), which will return a boolean indicating whether the arg has the specified type. You can also use the type() for type checking as mentioned in the theory.
