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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 eigtransform(self, sequences, right=True, mode='clip'):
 r"""Transform a list of sequences by projecting the sequences onto the first `n_timescales` dynamical eigenvectors. Parameters sequences : list of array-like List of sequences, or a single sequence. Each sequence should be a 1D iterable of state labels. Labels can be integers, strings, or other orderable objects. right : bool Which eigenvectors to map onto. Both the left (:math:`\Phi`) and the right (:math`\Psi`) eigenvectors of the transition matrix are commonly used, and differ in their normalization. The two sets of eigenvectors are related by the stationary distribution :: \Phi_i(x) = \Psi_i(x) * \mu(x) In the MSM literature, the right vectors (default here) are approximations to the transfer operator eigenfunctions, whereas the left eigenfunction are approximations to the propagator eigenfunctions. For more details, refer to reference [1]. mode : {'clip', 'fill'} Method by which to treat labels in `sequences` which do not have a corresponding index. This can be due, for example, to the ergodic trimming step. ``clip`` Unmapped labels are removed during transform. If they occur at the beginning or end of a sequence, the resulting transformed sequence will be shorted. If they occur in the middle of a sequence, that sequence will be broken into two (or more) sequences. (Default) ``fill`` Unmapped labels will be replaced with NaN, to signal missing data. [The use of NaN to signal missing data is not fantastic, but it's consistent with current behavior of the ``pandas`` library.] Returns ------- transformed : list of 2d arrays Each element of transformed is an array of shape ``(n_samples, n_timescales)`` containing the transformed data. References .. [1] Prinz, Jan-Hendrik, et al. "Markov models of molecular kinetics: Generation and validation." J. Chem. Phys. 134.17 (2011):
 174105. """


        result = []
        for y in self.transform(sequences, mode=mode):
            if right:
                op = self.right_eigenvectors_[:, 1:]
            else:
                op = self.left_eigenvectors_[:, 1:]

            is_finite = np.isfinite(y)
            if not np.all(is_finite):
                value = np.empty((y.shape[0], op.shape[1]))
                value[is_finite, :] = np.take(op, y[is_finite].astype(np.int), axis=0)
                value[~is_finite, :] = np.nan
            else:
                value = np.take(op, y, axis=0)
            result.append(value)

        return result