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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 Kurt(poly, dist=None, fisher=True, **kws):
 """ Kurtosis operator. Element by element 4rd order statistics of a distribution or polynomial. Args: poly (Poly, Dist):
 Input to take kurtosis on. dist (Dist):
 Defines the space the skewness is taken on. It is ignored if ``poly`` is a distribution. fisher (bool):
 If True, Fisher's definition is used (Normal -> 0.0). If False, Pearson's definition is used (normal -> 3.0) Returns: (numpy.ndarray):
 Element for element variance along ``poly``, where ``skewness.shape==poly.shape``. Examples: [6. 0.] [9. 3.] [nan 6. 0. 15.] """

    if isinstance(poly, distributions.Dist):
        x = polynomials.variable(len(poly))
        poly, dist = x, poly
    else:
        poly = polynomials.Poly(poly)

    if fisher:
        adjust = 3
    else:
        adjust = 0

    shape = poly.shape
    poly = polynomials.flatten(poly)

    m1 = E(poly, dist)
    m2 = E(poly**2, dist)
    m3 = E(poly**3, dist)
    m4 = E(poly**4, dist)

    out = (m4-4*m3*m1 + 6*m2*m1**2 - 3*m1**4) /\
            (m2**2-2*m2*m1**2+m1**4) - adjust

    out = numpy.reshape(out, shape)
    return out