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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 inner(X, Y, ip_B=None):
    '''Euclidean and non-Euclidean inner product.

    numpy.vdot only works for vectors and numpy.dot does not use the conjugate
    transpose.

    :param X: numpy array with ``shape==(N,m)``
    :param Y: numpy array with ``shape==(N,n)``
    :param ip_B: (optional) May be one of the following

        * ``None``: Euclidean inner product.
        * a self-adjoint and positive definite operator :math:`B` (as
          ``numpy.array`` or ``LinearOperator``). Then :math:`X^*B Y` is
          returned.
        * a callable which takes 2 arguments X and Y and returns
          :math:`\\langle X,Y\\rangle`.

    **Caution:** a callable should only be used if necessary. The choice
    potentially has an impact on the round-off behavior, e.g. of projections.

    :return: numpy array :math:`\\langle X,Y\\rangle` with ``shape==(m,n)``.
    '''
    if ip_B is None or isinstance(ip_B, IdentityLinearOperator):
        return numpy.dot(X.T.conj(), Y)
    (N, m) = X.shape
    (_, n) = Y.shape
    try:
        B = get_linearoperator((N, N), ip_B)
    except TypeError:
        return ip_B(X, Y)
    if m > n:
        return numpy.dot((B*X).T.conj(), Y)
    else:
        return numpy.dot(X.T.conj(), B*Y)