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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 get_min_eig_vec_proxy(self, use_tf_eig=False):
 """Computes the min eigen value and corresponding vector of matrix M. Args: use_tf_eig: Whether to use tf's default full eigen decomposition Returns: eig_vec: Minimum absolute eigen value eig_val: Corresponding eigen vector """

    if use_tf_eig:
      # If smoothness parameter is too small, essentially no smoothing
      # Just output the eigen vector corresponding to min
      return tf.cond(self.smooth_placeholder < 1E-8,
                     self.tf_min_eig_vec,
                     self.tf_smooth_eig_vec)

    # Using autograph to automatically handle
    # the control flow of minimum_eigen_vector
    min_eigen_tf = autograph.to_graph(utils.minimum_eigen_vector)

    def _vector_prod_fn(x):
      return self.dual_object.get_psd_product(x)

    estimated_eigen_vector = min_eigen_tf(
        x=self.eig_init_vec_placeholder,
        num_steps=self.eig_num_iter_placeholder,
        learning_rate=self.params['eig_learning_rate'],
        vector_prod_fn=_vector_prod_fn)
    return estimated_eigen_vector