code: def get_indexing(string):
      """
      Parse numpy-like fancy indexing from a string.
      Args:
          string (str): string represent the indices to take
              a subset of from array. Indices for each dimension
              are separated by `,`; indices for different dimensions
              are separated by `;`.
              e.g.: For a numpy array `arr` of shape (3,3,3), the string "1,2;1,2"
              means taking the sub-array `arr[[1,2], [1,2]]
      Returns:
          final_indexing (tuple): the parsed indexing.
      """
      index_ls = string.strip().split(";")
      final_indexing = []
      for index in index_ls:
          index_single_dim = index.split(",")
          index_single_dim = [int(i) for i in index_single_dim]
          final_indexing.append(index_single_dim)
  
      return tuple(final_indexing)
  
imports: import itertools
  import numpy
  import matplotlib.pyplot
  import torch
  from sklearn.metrics import confusion_matrix
  import slowfast.utils.logging
  from slowfast.datasets.utils import pack_pathway_output, tensor_normalize
label_desc: Use image node_id 4 for calling a global function with example usage: get_indexing(string) and returns: tuple