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DS-1000 / 450 / Say I have a 3 dimensional numpy array:

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Problem:
Say I have a 3 dimensional numpy array:
np.random.seed(1145)
A = np.random.random((5,5,5))
and I have two lists of indices corresponding to the 2nd and 3rd dimensions:
second = [1,2]
third = [3,4]
and I want to select the elements in the numpy array corresponding to
A[:][second][third]
so the shape of the sliced array would be (5,2,2) and
A[:][second][third].flatten()
would be equivalent to to:
In [226]:
for i in range(5):
    for j in second:
        for k in third:
            print A[i][j][k]
0.556091074129
0.622016249651
0.622530505868
0.914954716368
0.729005532319
0.253214472335
0.892869371179
0.98279375528
0.814240066639
0.986060321906
0.829987410941
0.776715489939
0.404772469431
0.204696635072
0.190891168574
0.869554447412
0.364076117846
0.04760811817
0.440210532601
0.981601369658
Is there a way to slice a numpy array in this way? So far when I try A[:][second][third] I get IndexError: index 3 is out of bounds for axis 0 with size 2 because the [:] for the first dimension seems to be ignored.
A:
<code>
import numpy as np
a = np.random.rand(5, 5, 5)
second = [1, 2]
third = [3, 4]
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Numpy

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Source and history

upstream_id / Original problem ID
450
task_id
804e0d5c-111a-5a28-9c9b-412680f4fce8
task_key
default--test--450
task_revision_id
1
config / split
default / test

Official source

initial import