code: def generate_pred_probs(self, N, K, labels, noisy=False):
      pred_probs = np.full((N, K), 0.1)
      pred_probs[np.arange(N), labels] = 0.9
      pred_probs = pred_probs / np.sum(
          pred_probs, axis=-1, keepdims=True
      )
      if (
          noisy
      ):  # Swap columns of a class to generate incorrect predictions
          pred_probs_slice = pred_probs[labels == 0]
          pred_probs_slice[:, [0, 1]] = pred_probs_slice[:, [1, 0]]
          pred_probs[labels == 0] = pred_probs_slice
      return pred_probs
  
file_path: cleanlab/tests/datalab/issue_manager/test_underperforming_group.py
imports: import numpy
  import pytest
  import pandas
  import scipy.sparse
  from cleanlab.datalab.internal.issue_manager.underperforming_group import UnderperformingGroupIssueManager
  from sklearn.datasets import make_blobs, load_iris
label_desc: Use image node_id 1 for calling the TestUnderperformingGroupIssueManager obj's underlying member method code with example usage: obj.generate_pred_probs(N, K, labels, noisy) and returns: pred_probs