code: def latency() -> EvaluationMetric:
      """
      This function will create a metric for calculating latency. Latency is determined by the time
      it takes to generate a prediction for a given input. Note that computing latency requires
      each row to be predicted sequentially, which will likely slow down the evaluation process.
      """
      return make_metric(
          eval_fn=lambda x: MetricValue(),
          greater_is_better=False,
          name="latency",
      )
  
imports: from mlflow.metrics.base import MetricValue
  from mlflow.metrics.metric_definitions import _accuracy_eval_fn, _ari_eval_fn, _f1_score_eval_fn, _flesch_kincaid_eval_fn, _mae_eval_fn, _mape_eval_fn, _max_error_eval_fn, _mse_eval_fn, _ndcg_at_k_eval_fn, _precision_at_k_eval_fn, _precision_eval_fn, _r2_score_eval_fn, _recall_at_k_eval_fn, _recall_eval_fn, _rmse_eval_fn, _rouge1_eval_fn, _rouge2_eval_fn, _rougeL_eval_fn, _rougeLsum_eval_fn, _token_count_eval_fn, _toxicity_eval_fn
  from mlflow.models import EvaluationMetric, make_metric
  from mlflow.utils.annotations import experimental
label_desc: Use image node_id 1 for calling a global function with example usage: latency() and returns: make_metric