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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def regularize_array(A):
 """ Takes an np.ndarray as an input. - If the array is one-dimensional, it's assumed to be an array of input values. - If the array is more than one-dimensional, its last index is assumed to curse over spatial dimension. Either way, the return value is at least two dimensional. A.shape[-1] gives the number of spatial dimensions. """

    if not isinstance(A,np.ndarray):
        A = np.array(A, dtype=float)
    else:
        A = np.asarray(A, dtype=float)

    if len(A.shape) <= 1:
        return A.reshape(-1,1)

    elif A.shape[-1]>1:
        return A.reshape(-1, A.shape[-1])

    else:
        return A