code: def multilabel_mock_dataset_and_probabilities(
      tweet_emotion_train_test_textdata,
  ):
      """Mock dataset and probabilities for multilabel classification"""
      from sklearn.datasets import make_multilabel_classification
      from sklearn.linear_model import LogisticRegression
      from sklearn.model_selection import train_test_split
  
      X, y = make_multilabel_classification(
          n_samples=3_000,
          n_features=10,
          n_classes=3,
          n_labels=2,
          random_state=42,
      )
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=42
      )
      probabilities = np.zeros(y_test.shape)
      for label_dim in range(y.shape[1]):
          clf = LogisticRegression(random_state=42).fit(
              X_train, y_train[:, label_dim]
          )
          probabilities[:, label_dim] = clf.predict_proba(X_test)[:, 1]
      data = tweet_emotion_train_test_textdata[1].sample(
          len(probabilities), random_state=42
      )
      data._label = y_test
      return data, probabilities
  
imports: import random
  import typing
  import numpy
  import pytest
  from datasets import load_dataset
  from nltk import download
  from nltk.corpus import movie_reviews
  from deepchecks.nlp.datasets.classification import just_dance_comment_analysis, tweet_emotion
  from deepchecks.nlp.text_data import TextData
label_desc: Use image node_id 18 for calling a global function with example usage: multilabel_mock_dataset_and_probabilities(tweet_emotion_train_test_textdata) and returns: data,  probabilities