code: def expand_attribution_channel(
      a_batch: np.ndarray, x_batch: np.ndarray
  ):
      """
      Expand additional channel dimension(s) for attributions if needed.
  
      Parameters
      ----------
      x_batch: np.ndarray
          A np.ndarray which contains the input data that are explained.
      a_batch: np.ndarray
          An array which contains pre-computed attributions i.e., explanations.
  
      Returns
      -------
      np.ndarray
          A x_batch with dimensions matching those of a_batch.
      """
      if a_batch.shape[0] != x_batch.shape[0]:
          raise ValueError(
              f"a_batch and x_batch must have same number of batches ({a_batch.shape[0]} != {x_batch.shape[0]})"
          )
      if a_batch.ndim > x_batch.ndim:
          raise ValueError(
              f"a must not have greater ndim than x ({a_batch.ndim} > {x_batch.ndim})"
          )
  
      if a_batch.ndim == x_batch.ndim:
          return a_batch
      else:
          attr_axes = infer_attribution_axes(a_batch, x_batch)
  
          # TODO: Infer_attribution_axes currently returns dimensions w/o batch dimension.
          attr_axes = [a + 1 for a in attr_axes]
          expand_axes = [
              a for a in range(1, x_batch.ndim) if a not in attr_axes
          ]
  
          return np.expand_dims(a_batch, axis=tuple(expand_axes))
  
imports: import copy
  import re
  from importlib import util
  from typing import Any, Dict, Optional, Sequence, Tuple, Union, List, TypeVar
  import numpy
  from skimage.segmentation import slic, felzenszwalb
  from quantus.helpers import asserts
  from quantus.helpers.model.model_interface import ModelInterface
label_desc: Use image node_id 16 for calling a global function with example usage: expand_attribution_channel(a_batch, x_batch) and returns: a_batch, np