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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 cov_from_value(self, scaling):
 """Return a covariance matrix for the jump distribution using the actual value of the stochastic as a guess of their variance, divided by the `scaling` argument. Note that this is likely to return a poor guess. """

        rv = []
        for s in self.stochastics:
            rv.extend(np.ravel(s.value).copy())

        # Remove 0 values since this would lead to quite small jumps...
        arv = np.array(rv)
        arv[arv == 0] = 1.

        # Create a diagonal covariance matrix using the scaling factor.
        return np.eye(self.dim) * np.abs(arv) / scaling