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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 Sens_t(poly, dist, **kws):
 """ Variance-based decomposition AKA Sobol' indices Total effect sensitivity index Args: poly (Poly):
 Polynomial to find first order Sobol indices on. dist (Dist):
 The distributions of the input used in ``poly``. Returns: (numpy.ndarray) : First order sensitivity indices for each parameters in ``poly``, with shape ``(len(dist),) + poly.shape``. Examples: [[0. 1. 0. 0.57142857] [0. 0. 1. 0.57142857]] """

    dim = len(dist)
    if poly.dim < dim:
        poly = chaospy.poly.setdim(poly, len(dist))

    zero = [1]*dim
    out = numpy.zeros((dim,) + poly.shape, dtype=float)
    V = Var(poly, dist, **kws)
    for i in range(dim):
        zero[i] = 0
        out[i] = ((V-Var(E_cond(poly, zero, dist, **kws), dist, **kws)) /
                  (V+(V == 0))**(V!=0))
        zero[i] = 1
    return out