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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 canonicalize(self, modelparams):
 """ Truncates negative eigenvalues and from each state represented by a tensor of model parameter vectors, and renormalizes as appropriate. :param np.ndarray modelparams: Array of shape ``(n_states, dim**2)`` containing model parameter representations of each of ``n_states`` different states. :return: The same model parameter tensor with all states truncated to be positive operators. If :attr:`~TomographyModel.allow_subnormalized` is `False`, all states are also renormalized to trace one. """

        modelparams = np.apply_along_axis(self.trunc_neg_eigs, 1, modelparams)
        # Renormalizes particles if allow_subnormalized=False.
        if not self._allow_subnormalied:
            modelparams = self.renormalize(modelparams)

        return modelparams