code: def test_condition_percent_new_variants_pass():
      # Arrange
      base_data = {
          "col1": [
              "Deep",
              "deep",
              "deep!!!",
              "earth",
              "foo",
              "bar",
              "foo?",
          ]
      }
      tested_data = {
          "col1": [
              "Deep",
              "deep",
              "$deeP$",
              "earth",
              "foo",
              "bar",
              "foo?",
              "?deep",
          ]
      }
      check = StringMismatchComparison().add_condition_ratio_new_variants_less_or_equal(
          0.5
      )
      # Act
      result = check.conditions_decision(
          check.run(
              pd.DataFrame(data=tested_data),
              pd.DataFrame(data=base_data),
          )
      )
      # Assert
      assert_that(
          result,
          has_items(
              equal_condition_result(
                  is_pass=True,
                  details="Passed for 1 relevant column",
                  name="Ratio of new variants in test data is less or equal to 50%",
              )
          ),
      )
  
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 10 for calling a global function with example usage: test_condition_percent_new_variants_pass() without return types