code: def _batch_check(
      labels: LabelLike, pred_probs: np.ndarray, num_class: int
  ) -> np.ndarray:
      """
      Basic checks to ensure batch of data looks ok. For efficiency, this check is quite minimal.
  
      Returns
      -------
      labels : np.ndarray
        `labels` formatted as a 1D array.
      """
      batch_size = pred_probs.shape[0]
      labels = np.asarray(labels)
      if len(labels) != batch_size:
          raise ValueError(
              "labels and pred_probs must have same length"
          )
      if pred_probs.shape[1] != num_class:
          raise ValueError("num_class must equal pred_probs.shape[1]")
  
      return labels
  
imports: import numpy
  from typing import Optional, List, Tuple, Any
  from cleanlab.count import get_confident_thresholds, _reduce_issues
  from cleanlab.rank import find_top_issues, _compute_label_quality_scores
  from cleanlab.typing import LabelLike
  from cleanlab.internal.util import value_counts_fill_missing_classes
  from cleanlab.internal.constants import CONFIDENT_THRESHOLDS_LOWER_BOUND, FLOATING_POINT_COMPARISON, CLIPPING_LOWER_BOUND
  import platform
  import multiprocessing
label_desc: Use image node_id 5 for calling a global function with example usage: _batch_check(labels, pred_probs, num_class) and returns: labels