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Structured creates view of the array interpreting its minor axis as fully covered by a dtype.

It is similar to arr.view(dtype) + corresponding reshape, but does
not have limitations of ndarray.view(). For example:

  In [1]: a = np.arange(3*3, dtype=np.int32).reshape((3,3))

  In [2]: a
  Out[2]:
  array([[0, 1, 2],
         [3, 4, 5],
         [6, 7, 8]], dtype=int32)

  In [3]: b = a[:2,:2]

  In [4]: b
  Out[4]:
  array([[0, 1],
         [3, 4]], dtype=int32)

  In [5]: dtxy = np.dtype([('x', np.int32), ('y', np.int32)])

  In [6]: dtxy
  Out[6]: dtype([('x', '<i4'), ('y', '<i4')])

  In [7]: b.view(dtxy)
  ---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call last)
  <ipython-input-66-af98529aa150> in <module>()
  ----> 1 b.view(dtxy)

  ValueError: To change to a dtype of a different size, the array must be C-contiguous

  In [8]: stru...