code: def test_return_type_and_len_with_target(metric):
      predictions = pd.Series(["sentence not", "random text", "a", "c"])
      targets = pd.Series(["sentence not", "random text", "a", "c"])
  
      result = metric.eval_fn(predictions, targets)
      assert isinstance(result, MetricValue)
      if result.scores:
          assert len(result.scores) == len(predictions) == len(targets)
  
      predictions = pd.Series([])
      targets = pd.Series([])
      result = metric.eval_fn(predictions, targets)
      assert result is None
  
imports: from unittest import mock
  import pandas
  import pytest
  from mlflow.metrics import MetricValue, ari_grade_level, exact_match, f1_score, flesch_kincaid_grade_level, mae, mape, max_error, mse, ndcg_at_k, precision_at_k, precision_score, r2_score, recall_at_k, recall_score, rmse, rouge1, rouge2, rougeL, rougeLsum, toxicity
label_desc: Use image node_id 1 for calling a global function with example usage: test_return_type_and_len_with_target(metric) without return types