code: def test_double_column_specific_mix():
      # Arrange
      data = {"col1": ["1", "bar", "cat"], "col2": ["6", 66, 666.66]}
      dataframe = pd.DataFrame(data=data)
      # Act
      result = MixedDataTypes(columns=["col1"]).run(dataframe)
      # Assert
      assert_that(result.value, has_length(1))
      assert_that(
          result.value,
          has_entries(
              col1=has_entries(
                  {
                      "strings": close_to(0.66, 0.01),
                      "numbers": close_to(0.33, 0.01),
                  }
              )
          ),
      )
  
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 7 for calling a global function with example usage: test_double_column_specific_mix() without return types