code: def _reduce_issues(pred_probs, labels):
      """Returns a boolean mask denoting correct predictions or predictions within a margin around 0.5 for binary classification, suitable for filtering out indices in 'is_label_issue'."""
      pred_probs_copy = np.copy(
          pred_probs
      )  # Make a copy of the original array
      pred_probs_copy[
          np.arange(len(labels)), labels
      ] += FLOATING_POINT_COMPARISON
      pred = pred_probs_copy.argmax(axis=1)
      mask = pred == labels
      del pred_probs_copy  # Delete copy
      return mask
  
imports: from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import StratifiedKFold
  from sklearn.metrics import confusion_matrix
  import sklearn.base
  import numpy
  import warnings
  from typing import Tuple, Union, Optional
  from cleanlab.typing import LabelLike
  from cleanlab.internal.multilabel_utils import stack_complement, get_onehot_num_classes
  from cleanlab.internal.constants import TINY_VALUE, CONFIDENT_THRESHOLDS_LOWER_BOUND, FLOATING_POINT_COMPARISON
  from cleanlab.internal.util import value_counts_fill_missing_classes, clip_values, clip_noise_rates, round_preserving_row_totals, append_extra_datapoint, train_val_split, get_num_classes, get_unique_classes, is_torch_dataset, is_tensorflow_dataset
  from cleanlab.internal.latent_algebra import compute_inv_noise_matrix, compute_py, compute_noise_matrix_from_inverse
  from cleanlab.internal.validation import assert_valid_inputs, labels_to_array
label_desc: Use image node_id 3 for calling a global function with example usage: _reduce_issues(pred_probs, labels) and returns: mask