code: def test_fi_n_top(diabetes_split_dataset_and_model):
      train, val, clf = diabetes_split_dataset_and_model
      train = Dataset(
          train.data.copy(), label="target", cat_features=["sex"]
      )
      val = Dataset(
          val.data.copy(), label="target", cat_features=["sex"]
      )
      train.data.loc[train.data.index % 2 == 0, "age"] = "aaa"
      val.data.loc[val.data.index % 2 == 1, "age"] = "aaa!!"
      train.data.loc[train.data.index % 2 == 0, "bmi"] = "aaa"
      val.data.loc[val.data.index % 2 == 1, "bmi"] = "aaa!!"
      train.data.loc[train.data.index % 2 == 0, "bp"] = "aaa"
      val.data.loc[val.data.index % 2 == 1, "bp"] = "aaa!!"
      train.data.loc[train.data.index % 2 == 0, "sex"] = "aaa"
      val.data.loc[val.data.index % 2 == 1, "sex"] = "aaa!!"
      # Arrange
      check = StringMismatchComparison(n_top_columns=3)
      # Act
      result = check.run(test_dataset=train, train_dataset=val)
      # Assert - The display table is transposed so check length of columns
      assert_that(result.display[1].columns, has_length(3))
  
file_path: deepchecks/tests/tabular/checks/train_test_validation/string_mismatch_comparison_test.py
imports: import numpy
  import pandas
  from hamcrest import assert_that, equal_to, greater_than, has_entries, has_entry, has_items, has_length
  from deepchecks.tabular.checks import StringMismatchComparison
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 11 for calling a global function with example usage: test_fi_n_top(diabetes_split_dataset_and_model) without return types