code: def concatenate_minibatches(
      minibatch_results: Union[List[np.ndarray], List[List[np.ndarray]]]
  ) -> Union[np.ndarray, List[np.ndarray]]:
      """
      Merges the explanations computed on minibatches so that the distributed explainer returns the same output as the
      sequential version. If the type returned by the explainer is not supported by the function, expand this function
      by adding an appropriately named private function and use this function to check the input type and call it.
  
      Parameters
      ----------
      minibatch_results
          Explanations for each minibatch.
          
      Returns
      -------
      If the input is ``List[np.ndarray]``, a single `numpy` array obtained by concatenating `minibatch` results along \
      the 0th axis. 
      If the input is ``List[List[np.ndarray]]`` A list of `numpy` arrays obtained by concatenating arrays in with the \
      same position in the sublists along the 0th axis.
      """  # noqa W605
  
      if isinstance(minibatch_results[0], np.ndarray):
          return np.concatenate(minibatch_results, axis=0)
      elif isinstance(minibatch_results[0], list) and isinstance(
          minibatch_results[0][0], np.ndarray
      ):
          return _array_list_concatenator(minibatch_results)  # type: ignore
      else:
          raise TypeError(
              "Minibatch concatenation function is defined only for List[np.ndarray] and List[List[np.ndarray]]"
          )
  
imports: import copy
  import logging
  from functools import partial
  from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union
  import numpy
  from scipy import sparse
  import ray
label_desc: Use image node_id 3 for calling a global function with example usage: concatenate_minibatches(minibatch_results) and returns: np, _array_list_concatenator