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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 tf_smooth_eig_vec(self):
 """Function that returns smoothed version of min eigen vector."""

    _, matrix_m = self.dual_object.get_full_psd_matrix()
    # Easier to think in terms of max so negating the matrix
    [eig_vals, eig_vectors] = tf.self_adjoint_eig(-matrix_m)
    exp_eig_vals = tf.exp(tf.divide(eig_vals, self.smooth_placeholder))
    scaling_factor = tf.reduce_sum(exp_eig_vals)
    # Multiplying each eig vector by exponential of corresponding eig value
    # Scaling factor normalizes the vector to be unit norm
    eig_vec_smooth = tf.divide(
        tf.matmul(eig_vectors, tf.diag(tf.sqrt(exp_eig_vals))),
        tf.sqrt(scaling_factor))
    return tf.reshape(
        tf.reduce_sum(eig_vec_smooth, axis=1),
        shape=[eig_vec_smooth.shape[0].value, 1])