code: def test_convert_long_to_wide():
      labels_long = make_data_long(data["labels"])
      labels_wide = convert_long_to_wide_dataset(labels_long)
  
      assert isinstance(labels_wide, pd.DataFrame)
  
      # ensures labels_long contains all the non-NaN values of labels_wide
      assert labels_wide.count(axis=1).sum() == len(labels_long)
  
      # checks one index to make sure data is consistent acrros both dataframes
      example_long = labels_long[labels_long["task"] == 0].sort_values(
          "annotator"
      )
      example_wide = labels_wide.iloc[0].dropna()
      assert all(example_long["annotator"] == example_wide.index)
      assert all(
          example_long["label"].reset_index(drop=True)
          == example_wide.reset_index(drop=True)
      )
  
imports: import numpy
  import pytest
  from copy import deepcopy
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab import count
  from cleanlab.multiannotator import get_label_quality_multiannotator, get_label_quality_multiannotator_ensemble, get_active_learning_scores, get_active_learning_scores_ensemble, get_majority_vote_label, convert_long_to_wide_dataset
  from cleanlab.internal.multiannotator_utils import format_multiannotator_labels
  import pandas
  from sklearn.linear_model import LogisticRegression
label_desc: Use image node_id 4 for calling a global function with example usage: test_convert_long_to_wide() without return types