code: def view(A, offset=0):
      
      from numpy.lib.stride_tricks import as_strided
      assert A.ndim == 2, "only implemented for 2 dimensions"
      assert A.shape[0] == A.shape[1], "attempting to get the view of non-square matrix?!"
      if offset > 0:
          return as_strided(A[0, offset:], shape=(A.shape[0] - offset, ), strides=((A.shape[0]+1)*A.itemsize, ))
      elif offset < 0:
          return as_strided(A[-offset:, 0], shape=(A.shape[0] + offset, ), strides=((A.shape[0]+1)*A.itemsize, ))
      else:
          return as_strided(A, shape=(A.shape[0], ), strides=((A.shape[0]+1)*A.itemsize, ))
docs: Get a view on the diagonal elements of a 2D array.
  
      This is actually a view (!) on the diagonal of the array, so you can
      in-place adjust the view.
  
      :param :class:`ndarray` A: 2 dimensional numpy array
      :param int offset: view offset to give back (negative entries allowed)
      :rtype: :class:`ndarray` view of diag(A)
  
      >>> import numpy as np
      >>> X = np.arange(9).reshape(3,3)
      >>> view(X)
      array([0, 4, 8])
      >>> d = view(X)
      >>> d += 2
      >>> view(X)
      array([ 2,  6, 10])
      >>> view(X, offset=-1)
      array([3, 7])
      >>> subtract(X, 3, offset=-1)
      array([[ 2,  1,  2],
             [ 0,  6,  5],
             [ 6,  4, 10]])