code: def general_off_policy_returns_from_q_and_v(
      q_t: Array,
      v_t: Array,
      r_t: Array,
      discount_t: Array,
      c_t: Array,
      stop_target_gradients: bool = False,
  ) -> Array:
      """Calculates targets for various off-policy evaluation algorithms.
  
      Given a window of experience of length `K+1`, generated by a behaviour policy
      μ, for each time-step `t` we can estimate the return `G_t` from that step
      onwards, under some target policy π, using the rewards in the trajectory, the
      values under π of states and actions selected by μ, according to equation:
  
        Gₜ = rₜ₊₁ + γₜ₊₁ * (vₜ₊₁ - cₜ₊₁ * q(aₜ₊₁) + cₜ₊₁* Gₜ₊₁),
  
      where, depending on the choice of `c_t`, the algorithm implements:
  
        Importance Sampling             c_t = π(x_t, a_t) / μ(x_t, a_t),
        Harutyunyan's et al. Q(lambda)  c_t = λ,
        Precup's et al. Tree-Backup     c_t = π(x_t, a_t),
        Munos' et al. Retrace           c_t = λ min(1, π(x_t, a_t) / μ(x_t, a_t)).
  
      See "Safe and Efficient Off-Policy Reinforcement Learning" by Munos et al.
      (https://arxiv.org/abs/1606.02647).
  
      Args:
        q_t: Q-values under π of actions executed by μ at times [1, ..., K - 1].
        v_t: Values under π at times [1, ..., K].
        r_t: rewards at times [1, ..., K].
        discount_t: discounts at times [1, ..., K].
        c_t: weights at times [1, ..., K - 1].
        stop_target_gradients: bool indicating whether or not to apply stop gradient
          to targets.
  
      Returns:
        Off-policy estimates of the generalized returns from states visited at times
        [0, ..., K - 1].
      """
      chex.assert_rank([q_t, v_t, r_t, discount_t, c_t], 1)
      chex.assert_type([q_t, v_t, r_t, discount_t, c_t], float)
      chex.assert_equal_shape(
          [q_t, v_t[:-1], r_t[:-1], discount_t[:-1], c_t]
      )
  
      g = r_t[-1] + discount_t[-1] * v_t[-1]  # G_K-1.
  
      def _body(acc, xs):
          reward, discount, c, v, q = xs
          acc = reward + discount * (v - c * q + c * acc)
          return acc, acc
  
      _, returns = jax.lax.scan(
          _body,
          g,
          (r_t[:-1], discount_t[:-1], c_t, v_t[:-1], q_t),
          reverse=True,
      )
      returns = jnp.concatenate([returns, g[jnp.newaxis]], axis=0)
  
      return jax.lax.select(
          stop_target_gradients, jax.lax.stop_gradient(returns), returns
      )
  
file_path: rlax/rlax/_src/multistep.py
imports: from typing import Union
  import chex
  import jax
  import jax.numpy
  from rlax._src import base
label_desc: Use image node_id 7 for calling a global function with example usage: general_off_policy_returns_from_q_and_v(q_t, v_t, r_t, discount_t, c_t, stop_target_gradients) and returns: jax, acc,  acc