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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 _yellowfin(self):
 """YellowFin auto-tuning optimizer based on momentum SGD. Returns: YF ops (Curvature range, Grad_variance, Dist_to_opt, Single-Step, Auto-Tuning) """

    # List for the returned Operations.
    yellowfin_ops = []

    # Curvature range ops.
    curv_range_ops = self._curvature_range()
    yellowfin_ops += curv_range_ops
    # Estimate of gradient Variance ops.
    grad_var_ops = self._grad_variance()
    yellowfin_ops += grad_var_ops
    # Distance to optimum ops.
    dist_to_opt_ops = self._dist_to_opt()
    yellowfin_ops += dist_to_opt_ops

    # Single-Step: minimizes the surrogate for the expected
    # squared distance from the optimum of a local quadratic
    # approximation after a single step while keeping all directions in the
    # robust region.
    self._mu = tf.identity(tf.cond(self._do_tune,
                                   self._get_mu_tensor,
                                   lambda: self._mu_var))
    with tf.control_dependencies([self._mu]):
      self._lr = tf.identity(tf.cond(self._do_tune,
                                     self._get_lr_tensor,
                                     lambda: self._lr_var))

    # Tune learning rate and momentum.
    with tf.control_dependencies([self._mu, self._lr]):
      self._mu = self._beta * self._mu_var + (1 - self._beta) * self._mu
      self._lr = self._beta * self._lr_var + (1 - self._beta) * self._lr
      yellowfin_ops.append(tf.assign(self._mu_var, self._mu))
      yellowfin_ops.append(tf.assign(self._lr_var, self._lr))

    yellowfin_ops = tf.group(*yellowfin_ops)
    return yellowfin_ops