code: def test_fi_n_top(diabetes_split_dataset_and_model):
      train, _, clf = diabetes_split_dataset_and_model
      train = Dataset(
          train.data.copy(), label="target", cat_features=["sex"]
      )
      train.data.loc[train.data.index % 4 == 1, "age"] = "a"
      train.data.loc[train.data.index % 4 == 1, "bmi"] = "a"
      train.data.loc[train.data.index % 4 == 1, "bp"] = "a"
      train.data.loc[train.data.index % 4 == 1, "sex"] = "a"
      # Arrange
      check = MixedDataTypes(n_top_columns=3)
      # Act
      result = check.run(train)
      # Assert - Display table is transposed so check columns length
      assert_that(result.display[1].columns, has_length(3))
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_entry, has_items, has_length, raises
  from deepchecks.core import ConditionCategory
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular.checks.data_integrity.mixed_data_types import MixedDataTypes
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 13 for calling a global function with example usage: test_fi_n_top(diabetes_split_dataset_and_model) without return types