code: def expected_results():
      return {
          "id": {
              "count": 9,
              "cv": check_is_NaN,
              "n_distinct": 8,
              "histogram": check_is_NaN,
              "iqr": check_is_NaN,
              "is_unique": False,
              "kurtosis": check_is_NaN,
              "mad": check_is_NaN,
              "max": check_is_NaN,
              "mean": check_is_NaN,
              "min": check_is_NaN,
              "mini_histogram": check_is_NaN,
              "n_missing": 0,
              "p_missing": 0.0,
              "p_distinct": 0.88888888,
              "p_zeros": check_is_NaN,
              "range": check_is_NaN,
              "skewness": check_is_NaN,
              "std": check_is_NaN,
              "sum": check_is_NaN,
              "variance": check_is_NaN,
          },
          "x": {
              "n": 9,
              "count": 9,
              "p_missing": 0.0,
              "n_distinct": 7,
              "n_unique": 5,
              "p_distinct": 0.7777777777777778,
              "is_unique": False,
              "p_unique": 0.5555555555555556,
              "n_infinite": 0,
              "p_infinite": 0.0,
              "n_zeros": 2,
              "p_zeros": 0.2222222222222222,
              "n_negative": 2,
              "p_negative": 0.2222222222222222,
              "5%": -10.0,
              "25%": -3.0,
              "50%": 0.0,
              "75%": 15.0,
              "95%": 50.0,
              "mad": 5.0,
              "min": -10.0,
              "max": check_is_NaN,
              "mean": check_is_NaN,
              "std": check_is_NaN,
              "variance": check_is_NaN,
              "kurtosis": check_is_NaN,
              "skewness": check_is_NaN,
              "sum": check_is_NaN,
              "range": check_is_NaN,
              "iqr": 18.0,
              "cv": check_is_NaN,
          },
          "y": {
              "n": 9,
              "count": 9,
              "p_missing": 0.0,
              "n_distinct": 9,
              "n_unique": 9,
              "p_distinct": 1.0,
              "is_unique": True,
              "p_unique": 1.0,
              "n_infinite": 0,
              "p_infinite": 0.0,
              "n_zeros": 0,
              "p_zeros": 0.0,
              "n_negative": 1,
              "p_negative": 0.1111111111111111,
              "5%": -3.1415926535,
              "25%": 1e-06,
              "50%": 15.9,
              "75%": 111.0,
              "95%": 3122.0,
              "mad": 15.9,
              "min": -3.1415926535,
              "max": check_is_NaN,
              "mean": check_is_NaN,
              "std": check_is_NaN,
              "variance": check_is_NaN,
              "kurtosis": check_is_NaN,
              "skewness": check_is_NaN,
              "sum": check_is_NaN,
              "range": check_is_NaN,
              "iqr": 110.999999,
              "cv": check_is_NaN,
          },
          "cat": {
              "n": 9,
              "count": 8,
              "p_missing": 0.1111111111111111,
              "n_distinct": 7,
              "n_unique": 6,
              "p_distinct": 0.875,
              "is_unique": False,
              "p_unique": 0.75,
          },
          "s1": {
              "n": 9,
              "count": 9,
              "p_missing": 0.0,
              "n_distinct": 1,
              "n_unique": 0,
              "p_distinct": 0.1111111111111111,
              "is_unique": False,
              "p_unique": 0.0,
              "n_infinite": 0,
              "p_infinite": 0.0,
              "n_zeros": 0,
              "p_zeros": 0.0,
              "n_negative": 0,
              "p_negative": 0.0,
              "5%": 1.0,
              "25%": 1.0,
              "50%": 1.0,
              "75%": 1.0,
              "95%": 1.0,
              "mad": 0.0,
              "min": 1.0,
              "max": 1.0,
              "mean": 1.0,
              "std": 0.0,
              "variance": 0.0,
              "kurtosis": check_is_NaN,
              "skewness": check_is_NaN,
              "sum": 9.0,
              "range": 0.0,
              "iqr": 0.0,
              "cv": 0.0,
          },
          "s2": {
              "count": 9,
              "cv": check_is_NaN,
              "n_distinct": 1,
              "histogram": check_is_NaN,
              "iqr": check_is_NaN,
              "is_unique": False,
              "kurtosis": check_is_NaN,
              "mad": check_is_NaN,
              "max": check_is_NaN,
              "mean": check_is_NaN,
              "min": check_is_NaN,
              "mini_histogram": check_is_NaN,
              "n_missing": 0,
              "p_missing": 0.0,
              "p_distinct": 0.1111111111111111,
              "p_zeros": check_is_NaN,
              "range": check_is_NaN,
              "skewness": check_is_NaN,
              "std": check_is_NaN,
              "sum": check_is_NaN,
              "variance": check_is_NaN,
          },
          "somedate": {
              "n": 9,
              "count": 8,
              "p_missing": 0.1111111111111111,
              "n_distinct": 6,
              "n_unique": 4,
              "p_distinct": 0.75,
              "is_unique": False,
              "p_unique": 0.5,
          },
          "bool_tf": {
              "count": 9,
              "n_distinct": 2,
              "is_unique": False,
              "n_missing": 0,
              "p_missing": 0,
              "p_distinct": 2 / 9,
          },
          "bool_tf_with_nan": {
              "n": 9,
              "count": 9,
              "p_missing": 0.0,
              "n_distinct": 2,
              "n_unique": 0,
              "p_distinct": 0.2222222222222222,
              "is_unique": False,
              "p_unique": 0.0,
          },
          "bool_01": {
              "n": 9,
              "count": 9,
              "cv": 0.9486832980505138,
              "n_distinct": 2,
              "iqr": 1.0,
              "is_unique": False,
              "mad": 0,
              "max": 1,
              "min": 0,
              "n_missing": 0,
              "p_missing": 0,
              "p_distinct": 2 / 9,
              "p_zeros": 4 / 9,
              "sum": 5,
          },
          "bool_01_with_nan": {
              "n": 9,
              "count": 9,
              "p_missing": 0.0,
              "n_distinct": 3,
              "n_unique": 1,
              "p_distinct": 0.3333333333333333,
              "is_unique": False,
              "p_unique": 0.1111111111111111,
              "n_infinite": 0,
              "p_infinite": 0.0,
              "n_zeros": 4,
              "p_zeros": 0.4444444444444444,
              "n_negative": 0,
              "p_negative": 0.0,
              "5%": 0.0,
              "25%": 0.0,
              "50%": 0.0,
              "75%": 1.0,
              "95%": 1.0,
              "mad": 0.0,
              "min": 0.0,
              "max": check_is_NaN,
              "mean": check_is_NaN,
              "std": check_is_NaN,
              "variance": check_is_NaN,
              "kurtosis": check_is_NaN,
              "skewness": check_is_NaN,
              "sum": check_is_NaN,
              "range": check_is_NaN,
              "iqr": 1.0,
              "cv": check_is_NaN,
          },
          "list": {
              "n": 9,
              "count": 9,
              "n_missing": 0,
              "p_missing": 0,
          },
          "mixed": {
              "n": 9,
              "count": 9,
              "n_missing": 0,
              "p_missing": 0,
          },
          "dict": {},
      }
  
file_path: ydata-profiling/tests/backends/spark_backend/test_descriptions_spark.py
imports: import datetime
  from dataclasses import asdict
  import numpy
  import pandas
  import pytest
  from ydata_profiling.config import SparkSettings
  from ydata_profiling.model.describe import describe
label_desc: Use image node_id 2 for calling a global function with example usage: expected_results() and returns: dict