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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 multi_log_probs_from_logits_and_actions(policy_logits, actions):
 """Computes action log-probs from policy logits and actions. In the notation used throughout documentation and comments, T refers to the time dimension ranging from 0 to T-1. B refers to the batch size and ACTION_SPACE refers to the list of numbers each representing a number of actions. Args: policy_logits: A list with length of ACTION_SPACE of float32 tensors of shapes [T, B, ACTION_SPACE[0]], [T, B, ACTION_SPACE[-1]] with un-normalized log-probabilities parameterizing a softmax policy. actions: A list with length of ACTION_SPACE of int32 tensors of shapes [T, B], [T, B] with actions. Returns: A list with length of ACTION_SPACE of float32 tensors of shapes [T, B], [T, B] corresponding to the sampling log probability of the chosen action w.r.t. the policy. """


    log_probs = []
    for i in range(len(policy_logits)):
        log_probs.append(-tf.nn.sparse_softmax_cross_entropy_with_logits(
            logits=policy_logits[i], labels=actions[i]))

    return log_probs