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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 susvd(x, x_obs, rho, penalties):
 """ Sequential unfolding SVD Parameters x : Tensor x_obs : array_like rho : float penalties : array_like penalty for each unfolding of the input tensor """


    assert type(x) == Tensor, "Input array must be a Tensor"

    while True:

        # proximal operator for the Fro. norm
        x = squared_error(x, rho, x_obs)

        # sequential singular value thresholding
        for ix, penalty in enumerate(penalties):
            x = x.unfold(ix).svt(penalty / rho).fold()

        yield x