code: def infer_attribution_axes(
      a_batch: np.ndarray, x_batch: np.ndarray
  ) -> Sequence[int]:
      """
      Infers the axes in x_batch that are covered by a_batch.
  
      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
          The axes inferred.
      """
      # TODO: Adapt for batched processing.
  
      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(
              "Attributions need to have <= dimensions than inputs, but {} > {}".format(
                  a_batch.ndim, x_batch.ndim
              )
          )
  
      # TODO: We currently assume here that the batch axis is not carried into the perturbation functions.
      a_shape = [s for s in np.shape(a_batch)[1:] if s != 1]
      x_shape = [s for s in np.shape(x_batch)[1:]]
  
      if a_shape == x_shape:
          return np.arange(0, len(x_shape))
  
      # One attribution value per sample
      if len(a_shape) == 0:
          return np.array([])
  
      x_subshapes = [
          [x_shape[i] for i in range(start, start + len(a_shape))]
          for start in range(0, len(x_shape) - len(a_shape) + 1)
      ]
      if x_subshapes.count(a_shape) < 1:
          # Check that attribution dimensions are (consecutive) subdimensions of inputs
          raise ValueError(
              "Attribution dimensions are not (consecutive) subdimensions of inputs:  "
              "inputs were of shape {} and attributions of shape {}".format(
                  x_batch.shape, a_batch.shape
              )
          )
      elif x_subshapes.count(a_shape) > 1:
          # Check that attribution dimensions are (unique) subdimensions of inputs.
          # Consider potentially expanded dims in attributions.
  
          if (
              a_batch.ndim == x_batch.ndim
              and len(a_shape) < a_batch.ndim
          ):
              a_subshapes = [
                  [
                      np.shape(a_batch)[1:][i]
                      for i in range(start, start + len(a_shape))
                  ]
                  for start in range(
                      0, len(np.shape(a_batch)[1:]) - len(a_shape) + 1
                  )
              ]
              if a_subshapes.count(a_shape) == 1:
                  # Inferring channel shape.
                  for dim in range(len(np.shape(a_batch)[1:]) + 1):
                      if a_shape == np.shape(a_batch)[1:][dim:]:
                          return np.arange(
                              dim, len(np.shape(a_batch)[1:])
                          )
                      if a_shape == np.shape(a_batch)[1:][:dim]:
                          return np.arange(0, dim)
  
              raise ValueError(
                  "Attribution axes could not be inferred for inputs of "
                  "shape {} and attributions of shape {}".format(
                      x_batch.shape, a_batch.shape
                  )
              )
  
          raise ValueError(
              "Attribution dimensions are not unique subdimensions of inputs:  "
              "inputs were of shape {} and attributions of shape {}."
              "Please expand attribution dimensions for a unique solution".format(
                  x_batch.shape, a_batch.shape
              )
          )
      else:
          # Infer attribution axes.
          for dim in range(len(x_shape) + 1):
              if a_shape == x_shape[dim:]:
                  return np.arange(dim, len(x_shape))
              if a_shape == x_shape[:dim]:
                  return np.arange(0, dim)
  
      raise ValueError(
          "Attribution axes could not be inferred for inputs of "
          "shape {} and attributions of shape {}".format(
              x_batch.shape, a_batch.shape
          )
      )
  
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 17 for calling a global function with example usage: infer_attribution_axes(a_batch, x_batch) and returns: np, np, np, np, np, np