code: def get_clean_prediction(target: ArrayLike, prediction: ArrayLike):
      """Create `prediction` that is of a form comparable to `target`
  
      Parameters
      ----------
      target: Array-like
          True labels for the data. Should be same shape as `prediction`
      prediction: Array-like
          Predicted labels for the data. Should be same shape as `target`
  
      Returns
      -------
      prediction: Array-like
          If `target` types are ints, and `prediction` types are not, given predicted labels clipped
          between the min, and max of `target`, then rounded to the nearest integer. Else, original
          predicted labels"""
      target_type = type_of_target(target)
      prediction_type = type_of_target(prediction)
      # ValueError probably: "Classification metrics can't handle a mix of binary and continuous targets"
      if _is_int(target) and not _is_int(prediction):
          #################### Get Minimum/Maximum ####################
          target_min, target_max = target.min(), target.max()
  
          with suppress(
              TypeError
          ):  # Bypass one-dimensional arrays, whose min/max should be a scalar
              if (len(target_min) == 1) and (len(target_max) == 1):
                  target_min, target_max = target_min[0], target_max[0]
  
          #################### Clip/Round `prediction` ####################
          try:
              prediction = np.clip(prediction, target_min, target_max)
          except ValueError:
              prediction = prediction.clip(
                  target_min, target_max, axis=1
              )
          finally:
              prediction = prediction.astype(np.float64)
              prediction = np.rint(prediction)
      elif (
          target_type in classification_target_types
          and prediction_type.startswith("continuous")
      ):
          prediction = classify_output(target, prediction)
  
      # TODO: One-hot-encoded outputs will be of type "multiclass-multioutput" - Handle it
      return prediction
  
imports: from collections import OrderedDict
  from contextlib import suppress
  import numpy
  import pandas
  from typing import Dict, Iterable, List, Tuple, Union
  from sklearn import metrics
  from sklearn.utils.multiclass import type_of_target, unique_labels
label_desc: Use image node_id 4 for calling a global function with example usage: get_clean_prediction(target, prediction) and returns: prediction