repeated indices in axes keyword for N-dimensional FFT are ignored

[vtavana]

Repeated indices in axes keyword are ignored while the transform over the repeated axis should be performed multiple times.
Result from stock NumPy

>>> import numpy, mkl_fft
>>> in_arr = [[5, 4, 6, 3, 7], [-1, -3, -4, -7, 0]]
>>> dtype = numpy.complex64
>>> a_np = numpy.array(in_arr, dtype=dtype)
>>> numpy.fft.fft2(a_np, axes=(0,1))   
# array([[10.       +0.j       ,  8.09017  +2.1796277j,
#        -3.09017  +9.23305j  , -3.09017  -9.23305j  ,
#         8.09017  -2.1796277j],
#       [40.       +0.j       , -5.854102 +0.j       ,
#         0.8541019+0.j       ,  0.8541019+0.j       ,
#        -5.854102 +0.j       ]], dtype=complex64)
		
>>> numpy.fft.fft2(a_np, axes=(0,1,1))
# array([[ 20.+0.j,  35.+0.j, -20.+0.j,  10.+0.j,   5.+0.j],
#       [ 30.+0.j,  35.+0.j,  50.+0.j,  50.+0.j,  35.+0.j]],
#      dtype=complex64)


>>> mkl_fft.fft2(a_np, axes=(0,1))   
# array([[10.        +0.j      ,  8.09017   +2.179628j,
#        -3.09017   +9.233051j, -3.09017   -9.233051j,
#         8.09017   -2.179628j],
#       [40.        +0.j      , -5.854102  +0.j      ,
#         0.85410213+0.j      ,  0.85410213+0.j      ,
#        -5.854102  +0.j      ]], dtype=complex64)

>>> mkl_fft.fft2(a_np, axes=(0,1,1)) # returns the same data as axes=(0,1)	 
# array([[10.        +0.j      ,  8.09017   +2.179628j,
#        -3.09017   +9.233051j, -3.09017   -9.233051j,
#         8.09017   -2.179628j],
#       [40.        +0.j      , -5.854102  +0.j      ,
#         0.85410213+0.j      ,  0.85410213+0.j      ,
#        -5.854102  +0.j      ]], dtype=complex64)