code: def simple_custom_plt_check():
      class DatasetSizeComparison(TrainTestCheck):
          """Check which compares the sizes of train and test datasets."""
  
          def run_logic(self, context: Context) -> CheckResult:
              # Check logic
              train_size = context.train.n_samples
              test_size = context.test.n_samples
  
              # Create the check result value
              sizes = {"Train": train_size, "Test": test_size}
              sizes_df_for_display = pd.DataFrame(sizes, index=["Size"])
  
              # Display function of matplotlib graph:
              def graph_display():
                  plt.bar(sizes.keys(), sizes.values(), color="green")
                  plt.xlabel("Dataset")
                  plt.ylabel("Size")
                  plt.title("Datasets Size Comparison")
  
              return CheckResult(
                  sizes, display=[sizes_df_for_display, graph_display]
              )
  
      return DatasetSizeComparison()
  
imports: import logging
  import random
  from typing import Tuple
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import load_diabetes, load_iris
  from sklearn.ensemble import AdaBoostClassifier, GradientBoostingRegressor, RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import KBinsDiscretizer
  from deepchecks.core.check_result import CheckResult
  from deepchecks.tabular import Context, Dataset, TrainTestCheck
  from deepchecks.tabular.datasets.classification import lending_club
  from deepchecks.utils.logger import set_verbosity
label_desc: Use image node_id 20 for calling a global function with example usage: simple_custom_plt_check() and returns: DatasetSizeComparison, CheckResult